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1. Optimization of techniques for multiple platform testing in small, precious samples such as human chorionic villus sampling

Author: Charles R. Farber, Margareta D. Pisarska, Seth Guller, Yii-Der Ida Chen, Jerome I. Rotter, Ning Xu, Erica T. Wang, Mark O. Goodarzi, Stephen S. Rich, Gillian M. Barlow, Marzieh Akhlaghpour, Aaron J. Mackey, John W Williams, and Bora Lee
Subjects: 0301 basic medicine, 030219 obstetrics & reproductive medicine, Microarray, medicine.diagnostic_test, Obstetrics and Gynecology, Chorionic villus sampling, RNA, Computational biology, Biology, Molecular biology, 03 medical and health sciences, chemistry.chemical_compound, 030104 developmental biology, 0302 clinical medicine, medicine.anatomical_structure, chemistry, Gene expression, medicine, Chorionic villi, Epigenetics, Gene, Genetics (clinical), DNA
Abstract: Background Multiple testing to understand global changes in gene expression based on genetic and epigenetic modifications is evolving. Chorionic villi, obtained for prenatal testing, is limited, but can be used to understand ongoing human pregnancies. However, optimal storage, processing and utilization of CVS for multiple platform testing has not been established. Results Leftover CVS samples were flash-frozen or preserved in RNAlater. Modifications to standard isolation kits were performed to isolate quality DNA and RNA from samples as small as 2-5 mg. RNAlater samples had significantly higher RNA yields and quality and were successfully used in microarray and RNA-sequencing (RNA-seq). RNA-seq libraries generated using 200 vs. 800 ng RNA showed similar biological coefficients of variation. RNAlater samples had lower DNA yields and quality, which improved by heating the elution buffer to 70 °C. Purification of DNA was not necessary for bisulfite-conversion and genome-wide methylation profiling. CVS cells were propagated and continue to express genes found in freshly-isolated chorionic villi. Conclusions CVS samples preserved in RNAlater are superior. Our optimized techniques provide specimens for genetic, epigenetic and gene expression studies from a single small sample which can be used to develop diagnostics and treatments using a systems biology approach in the prenatal period.
Published: 2016
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2. Targeted Deep Sequencing in Multiple-Affected Sibships of European Ancestry Identifies Rare Deleterious Variants in PTPN22 That Confer Risk for Type 1 Diabetes

Author: Stephen S. Rich, Jocyndra A. Wright, Patrick Concannon, Aaron J. Mackey, Suna Onengut-Gumuscu, Tania Habib, Aaron R. Quinlan, Jane H. Buckner, and Yan Ge
Subjects: CD4-Positive T-Lymphocytes, Male, 0301 basic medicine, endocrine system, Adolescent, Genotype, endocrine system diseases, Endocrinology, Diabetes and Metabolism, Immunoblotting, Biology, White People, Deep sequencing, Frameshift mutation, CSK Tyrosine-Protein Kinase, PTPN22, Young Adult, 03 medical and health sciences, immune system diseases, Internal Medicine, Humans, Immunoprecipitation, Missense mutation, Genetic Predisposition to Disease, Allele, Child, Genotyping, Gene, Alleles, Genetics, Reverse Transcriptase Polymerase Chain Reaction, Siblings, Genetic Variation, High-Throughput Nucleotide Sequencing, Infant, nutritional and metabolic diseases, Genetics/Genomes/Proteomics/Metabolomics, Protein Tyrosine Phosphatase, Non-Receptor Type 22, Sequence Analysis, DNA, Diabetes Mellitus, Type 1, src-Family Kinases, 030104 developmental biology, Case-Control Studies, Child, Preschool, Female
Abstract: Despite finding more than 40 risk loci for type 1 diabetes (T1D), the causative variants and genes remain largely unknown. Here, we sought to identify rare deleterious variants of moderate-to-large effects contributing to T1D. We deeply sequenced 301 protein-coding genes located in 49 previously reported T1D risk loci in 70 T1D cases of European ancestry. These cases were selected from putatively high-risk families that had three or more siblings diagnosed with T1D at early ages. A cluster of rare deleterious variants in PTPN22 was identified, including two novel frameshift mutations (ss538819444 and rs371865329) and two missense variants (rs74163663 and rs56048322). Genotyping in 3,609 T1D families showed that rs56048322 was significantly associated with T1D and that this association was independent of the T1D-associated common variant rs2476601. The risk allele at rs56048322 affects splicing of PTPN22, resulting in the production of two alternative PTPN22 transcripts and a novel isoform of LYP (the protein encoded by PTPN22). This isoform competes with the wild-type LYP for binding to CSK and results in hyporesponsiveness of CD4+ T cells to antigen stimulation in T1D subjects. These findings demonstrate that in addition to common variants, rare deleterious variants in PTPN22 exist and can affect T1D risk.
Published: 2015
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3. Combinatorial drug screening and molecular profiling reveal diverse mechanisms of intrinsic and adaptive resistance to BRAF inhibition in V600E BRAF mutant melanomas

Author: Devin G. Roller, Daniel Gioeli, Aaron J. Mackey, Brian J. Capaldo, Emanuel F. Petricoin, Stefan Bekiranov, Mark R. Conaway, and Michael J. Weber
Subjects: 0301 basic medicine, therapeutic resistance, Proto-Oncogene Proteins B-raf, Cell signaling, Indoles, MAP Kinase Signaling System, Drug Evaluation, Preclinical, Mice, Nude, Drug resistance, Mice, SCID, Lapatinib, Bioinformatics, Receptor tyrosine kinase, BRAF, 03 medical and health sciences, Mice, Cell Line, Tumor, Antineoplastic Combined Chemotherapy Protocols, medicine, melanoma, cell signaling, Animals, Humans, Protein Kinase Inhibitors, Sulfonamides, biology, business.industry, Melanoma, medicine.disease, Molecular medicine, 3. Good health, 030104 developmental biology, Oncology, Drug Resistance, Neoplasm, Cancer research, biology.protein, Quinazolines, business, human activities, V600E, MAP Kinase, medicine.drug, Research Paper
Abstract: // Devin G. Roller 1 , Brian Capaldo 2 , Stefan Bekiranov 2 , Aaron J. Mackey 3 , Mark R. Conaway 3 , Emanuel F. Petricoin 4 , Daniel Gioeli 1 , Michael J. Weber 1 1 Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, VA, 22908 USA 2 Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, 22908 USA 3 Department of Public Health Sciences, University of Virginia, Charlottesville, VA, 22908 USA 4 Center for Applied Proteomics and Molecular Medicine, School of Systems Biology, College of Science, George Mason University, Manassas, VA 20110, USA Correspondence to: Michael J. Weber, e-mail: mjw@virginia.edu Keywords: melanoma, therapeutic resistance, cell signaling, BRAF, MAP Kinase Received: August 20, 2015 Accepted: November 21, 2015 Published: December 10, 2015 ABSTRACT Over half of BRAFV600E melanomas display intrinsic resistance to BRAF inhibitors, in part due to adaptive signaling responses. In this communication we ask whether BRAFV600E melanomas share common adaptive responses to BRAF inhibition that can provide clinically relevant targets for drug combinations. We screened a panel of 12 treatment-naive BRAFV600E melanoma cell lines with MAP Kinase pathway inhibitors in pairwise combination with 58 signaling inhibitors, assaying for synergistic cytotoxicity. We found enormous diversity in the drug combinations that showed synergy, with no two cell lines having an identical profile. Although the 6 lines most resistant to BRAF inhibition showed synergistic benefit from combination with lapatinib, the signaling mechanisms by which this combination generated synergistic cytotoxicity differed between the cell lines. We conclude that adaptive responses to inhibition of the primary oncogenic driver (BRAFV600E) are determined not only by the primary oncogenic driver but also by diverse secondary genetic and epigenetic changes (“back-seat drivers”) and hence optimal drug combinations will be variable. Because upregulation of receptor tyrosine kinases is a major source of drug resistance arising from diverse adaptive responses, we propose that inhibitors of these receptors may have substantial clinical utility in combination with inhibitors of the MAP Kinase pathway.
Published: 2015

4. An Islet-Targeted Genome-Wide Association Scan Identifies Novel Genes Implicated in Cytokine-Mediated Islet Stress in Type 2 Diabetes

Author: Lynne E. Wagenknecht, Richard M. Watanabe, Aaron J. Mackey, Poonam Sharma, Kent D. Taylor, Nicholette D. Palmer, Eden A. Dejene, Carl D. Langefeld, James W. Ramadan, Craig S. Nunemaker, and Stephen S. Rich
Subjects: Male, endocrine system, medicine.medical_specialty, Candidate gene, endocrine system diseases, medicine.medical_treatment, Interleukin-1beta, Population, Single-nucleotide polymorphism, Type 2 diabetes, Biology, Diabetes Mellitus, Experimental, Islets of Langerhans, Mice, Endocrinology, Receptor-Interacting Protein Serine-Threonine Kinase 2, Stress, Physiological, Internal medicine, medicine, Animals, Humans, education, Original Research, Oligonucleotide Array Sequence Analysis, Inflammation, education.field_of_study, geography, geography.geographical_feature_category, Factor XIII, Interleukin-6, Microarray analysis techniques, Gene Expression Profiling, Serine Endopeptidases, Membrane Proteins, Islet, medicine.disease, Cytokine, Diabetes Mellitus, Type 2, Lipotoxicity, Receptor-Interacting Protein Serine-Threonine Kinases, Serine Proteases, Carrier Proteins, Genome-Wide Association Study
Abstract: Genome-wide association studies in human type 2 diabetes (T2D) have renewed interest in the pancreatic islet as a contributor to T2D risk. Chronic low-grade inflammation resulting from obesity is a risk factor for T2D and a possible trigger of β-cell failure. In this study, microarray data were collected from mouse islets after overnight treatment with cytokines at concentrations consistent with the chronic low-grade inflammation in T2D. Genes with a cytokine-induced change of >2-fold were then examined for associations between single nucleotide polymorphisms and the acute insulin response to glucose (AIRg) using data from the Genetics Underlying Diabetes in Hispanics (GUARDIAN) Consortium. Significant evidence of association was found between AIRg and single nucleotide polymorphisms in Arap3 (5q31.3), F13a1 (6p25.3), Klhl6 (3q27.1), Nid1 (1q42.3), Pamr1 (11p13), Ripk2 (8q21.3), and Steap4 (7q21.12). To assess the potential relevance to islet function, mouse islets were exposed to conditions modeling low-grade inflammation, mitochondrial stress, endoplasmic reticulum (ER) stress, glucotoxicity, and lipotoxicity. RT-PCR revealed that one or more forms of stress significantly altered expression levels of all genes except Arap3. Thapsigargin-induced ER stress up-regulated both Pamr1 and Klhl6. Three genes confirmed microarray predictions of significant cytokine sensitivity: F13a1 was down-regulated 3.3-fold by cytokines, Ripk2 was up-regulated 1.5- to 3-fold by all stressors, and Steap4 was profoundly cytokine sensitive (167-fold up-regulation). Three genes were thus closely associated with low-grade inflammation in murine islets and also with a marker for islet function (AIRg) in a diabetes-prone human population. This islet-targeted genome-wide association scan identified several previously unrecognized candidate genes related to islet dysfunction during the development of T2D.
Published: 2015
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5. Pharmacotoxicology of Clinically-Relevant Concentrations of Obeticholic Acid in an Organotypic Human Hepatocyte System

Author: David Manka, Brian R. Wamhoff, Arun J. Sanyal, Banumathi K. Cole, Robert A. Figler, Ryan E. Feaver, S. Marukian, Aaron J. Mackey, Brett R. Blackman, Ajit Dash, Mark J. Lawson, Maria Sol Collado, and Stephen A. Hoang
Subjects: 0301 basic medicine, Agonist, medicine.medical_specialty, Pharmacotoxicology, medicine.drug_class, Cell Survival, medicine.medical_treatment, Biology, Toxicology, Chenodeoxycholic Acid, Article, 03 medical and health sciences, chemistry.chemical_compound, Non-alcoholic Fatty Liver Disease, Internal medicine, medicine, Humans, Cells, Cultured, Cholesterol, Insulin, Obeticholic acid, FGF19, General Medicine, 030104 developmental biology, Endocrinology, chemistry, Hepatocytes, Farnesoid X receptor, Signal transduction, Transcriptome
Abstract: Nonalcoholic steatohepatitis (NASH) is an emerging health crisis with no approved therapies. Obeticholic acid (OCA), a farnesoid X receptor (FXR) agonist, shows promise in NASH trials. However, the precise mechanisms mediating OCA effects and impact on cholesterol metabolism are not fully understood. We explored the pharmaco-toxicological effects of OCA on patho-physiological pathways in hepatocytes using a previously described perfused organotypic liver system that allows culture in near-physiological insulin/glucose milieus, and exhibits drug responses at clinically-relevant concentrations. Primary hepatocytes experienced 48-hour exposure to OCA at concentrations approximating therapeutic (0.5μM) and supratherapeutic (10μM) levels. Global transcriptomics by RNAseq was complimented by cellular viability (MTT), CYP activity assays, and secreted FGF19 levels in the media. Dose-dependent, transcriptional effects suggested suppression of bile acid synthesis (↓CYP7A1, ↓CYP27A1) and increased bile efflux (↑ABCB4, ↑ABCB11, ↑OSTA, ↑OSTB). Pleiotropic effects included suppression of TGFβ and IL-6 signaling pathways, and signatures suggestive of HDL suppression (↑SCARB1, ↓ApoAI, ↓LCAT) and LDL elevation (↑ApoB, ↓CYP7A1). OCA exhibited direct FXR-mediated effects with increased FGF19 secretion. Transcriptomics revealed regulation of metabolic, anti-inflammatory, and anti-fibrotic pathways beneficial in NASH, and predicted cholesterol profiles consistent with clinical findings. Follow-up studies under lipotoxic/inflammatory conditions would corroborate these effects in a disease-relevant environment.
Published: 2016

6. Synthetic Lethal Screening with Small-Molecule Inhibitors Provides a Pathway to Rational Combination Therapies for Melanoma

Author: Michael J. Weber, Brian J. Capaldo, Mark J. Axelrod, Karin Jensen, Aaron J. Mackey, Devin G. Roller, and Daniel Gioeli
Subjects: Niacinamide, Sorafenib, MAPK/ERK pathway, Cancer Research, Diclofenac, Skin Neoplasms, Cell, Phospholipases A2, Cytosolic, Antineoplastic Agents, Pharmacology, Biology, Article, Small Molecule Libraries, Cell Line, Tumor, Antineoplastic Combined Chemotherapy Protocols, medicine, Humans, Extracellular Signal-Regulated MAP Kinases, Melanoma, Cell Death, Genome, Human, Phenylurea Compounds, MEK inhibitor, Drug Synergism, Small molecule, Gene Expression Regulation, Neoplastic, medicine.anatomical_structure, Oncology, biology.protein, Cyclooxygenase, Signal transduction, Signal Transduction, Genetic screen, medicine.drug
Abstract: Recent data show that extracellular signals are transmitted through a network of proteins rather than hierarchical signaling pathways, suggesting that the inhibition of a single component of a canonical pathway is insufficient for the treatment of cancer. The biologic outcome of signaling through a network is inherently more robust and resistant to inhibition of a single network component. In this study, we conducted a functional chemical genetic screen to identify novel interactions between signaling inhibitors that would not be predicted on the basis of our current understanding of signaling networks. We screened over 300 drug combinations in nine melanoma cell lines and have identified pairs of compounds that show synergistic cytotoxicity. The synergistic cytotoxicities identified did not correlate with the known RAS and BRAF mutational status of the melanoma cell lines. Among the most robust results was synergy between sorafenib, a multikinase inhibitor with activity against RAF, and diclofenac, a nonsteroidal anti-inflammatory drug (NSAID). Drug substitution experiments using the NSAIDs celecoxib and ibuprofen or the MAP–ERK kinase inhibitor PD325901 and the RAF inhibitor RAF265 suggest that inhibition of COX and mitogen-activated protein kinase signaling are targets for the synergistic cytotoxicity of sorafenib and diclofenac. Cotreatment with sorafenib and diclofenac interrupts a positive feedback signaling loop involving extracellular signal–regulated kinase, cellular phospholipase A2, and COX. Genome-wide expression profiling shows synergy-specific downregulation of survival-related genes. This study has uncovered novel functional drug combinations and suggests that the underlying signaling networks that control responses to targeted agents can vary substantially, depending on unexplored components of the cell genotype. Mol Cancer Ther; 11(11); 2505–15. ©2012 AACR.
Published: 2012
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7. Leptin Protects Host Cells from Entamoeba histolytica Cytotoxicity by a STAT3-Dependent Mechanism

Author: Aaron J. Mackey, Shom N. Paul, William A. Petri, Chelsea Marie, and Hans P. Verkerke
Subjects: Leptin, STAT3 Transcription Factor, Immunology, Apoptosis, Microbiology, Immune system, Humans, SOCS3, Receptor, STAT5, Host Response and Inflammation, Leptin receptor, biology, Reverse Transcriptase Polymerase Chain Reaction, Entamoeba histolytica, digestive, oral, and skin physiology, Molecular biology, Cell biology, HEK293 Cells, Infectious Diseases, Gene Expression Regulation, Mutation, biology.protein, Parasitology, Signal transduction, hormones, hormone substitutes, and hormone antagonists, Signal Transduction, Proto-oncogene tyrosine-protein kinase Src
Abstract: The adipocytokine leptin links nutritional status to immune function. Leptin signaling protects from amebiasis, but the molecular mechanism is not understood. We developed an in vitro model of ameba-host cell interaction to test the hypothesis that leptin prevents ameba-induced apoptosis in host epithelial cells. We demonstrated that activation of mammalian leptin signaling increased cellular resistance to amebic cytotoxicity, including caspase-3 activation. Exogenous expression of the leptin receptor conferred resistance in susceptible cells, and leptin stimulation enhanced protection. A series of leptin receptor signaling mutants showed that resistance to amebic cytotoxicity was dependent on activation of STAT3 but not the Src homology-2 domain-containing tyrosine phosphatase (SHP-2) or STAT5. A common polymorphism in the leptin receptor (Q223R) that increases susceptibility to amebiasis in humans and mice was found to increase susceptibility to amebic cytotoxicity in single cells. The Q223R polymorphism also decreased leptin-dependent STAT3 activation by 21% relative to that of the wild-type (WT) receptor ( P = 0.035), consistent with a central role of STAT3 signaling in protection. A subset of genes uniquely regulated by STAT3 in response to leptin was identified. Most notable were the TRIB1 and suppressor of cytokine signaling 3 (SOCS3) genes, which have opposing roles in the regulation of apoptosis. Overall apoptotic genes were highly enriched in this gene set ( P < 1E−05), supporting the hypothesis that leptin regulation of host apoptotic genes via STAT3 is responsible for protection. This is the first demonstration of a mammalian signaling pathway that restricts amebic pathogenesis and represents an important advance in our mechanistic understanding of how leptin links nutrition and susceptibility to infection.
Published: 2012
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8. Insights into evolution of multicellular fungi from the assembled chromosomes of the mushroom Coprinopsis cinerea ( Coprinus cinereus )

Author: Patricia J. Pukkila, Claire Burns, Mark L. Farman, Gerard Manning, Björn Canbäck, Ashleigh Huggins, Heather J. Palmerini, Hoi Shan Kwan, Aaron J. Mackey, David C. Fargo, Jixin Deng, Chinnapa Kodira, Vardges Ter-Hovhannisyan, Allen C. Gathman, Chun Hang Au, Sreedhar Kilaru, Dag Ahrén, Jonathan M. Goldberg, Fred S. Dietrich, Donald O. Natvig, Patrick J. Hoegger, Mario Stanke, Miriam E. Zolan, Bruce W. Birren, Alexandre Lomsadze, Weixi Li, Bruce A. Roe, Hajime Muraguchi, Sarah K. Wilke, Doris M. Kupfer, Roderic Guigó, Cathy J. Rehmeyer, Ursula Kües, Francis Martin, Walt W. Lilly, Narmada Shenoy, Chi Keung Cheng, Anders Tunlid, Takashi Kamada, Lorna A. Casselton, Li-Jun Ma, Mark Borodovsky, James B. Hooker, Timothy Y. James, Rajesh Velagapudi, Qiandong Zeng, Jason E. Stajich, and Marilee A. Ramesh
Subjects: Retroelements, Molecular Sequence Data, Coprinus, Genome, Evolution, Molecular, Fungal Proteins, 03 medical and health sciences, Cytochrome P-450 Enzyme System, Meiosis, Gene Duplication, Commentaries, Gene family, Gene, Phylogeny, DNA Primers, 030304 developmental biology, Synteny, Recombination, Genetic, Genetics, 0303 health sciences, Multidisciplinary, Base Sequence, biology, 030306 microbiology, fungi, Fungal genetics, Chromosome Mapping, RNA, Fungal, biology.organism_classification, Coprinopsis cinerea, Multigene Family, Chromosomes, Fungal, Genome, Fungal, Protein Kinases
Abstract: The mushroom Coprinopsis cinerea is a classic experimental model for multicellular development in fungi because it grows on defined media, completes its life cycle in 2 weeks, produces some 10 8 synchronized meiocytes, and can be manipulated at all stages in development by mutation and transformation. The 37-megabase genome of C. cinerea was sequenced and assembled into 13 chromosomes. Meiotic recombination rates vary greatly along the chromosomes, and retrotransposons are absent in large regions of the genome with low levels of meiotic recombination. Single-copy genes with identifiable orthologs in other basidiomycetes are predominant in low-recombination regions of the chromosome. In contrast, paralogous multicopy genes are found in the highly recombining regions, including a large family of protein kinases (FunK1) unique to multicellular fungi. Analyses of P450 and hydrophobin gene families confirmed that local gene duplications drive the expansions of paralogous copies and the expansions occur in independent lineages of Agaricomycotina fungi. Gene-expression patterns from microarrays were used to dissect the transcriptional program of dikaryon formation (mating). Several members of the FunK1 kinase family are differentially regulated during sexual morphogenesis, and coordinate regulation of adjacent duplications is rare. The genomes of C. cinerea and Laccaria bicolor , a symbiotic basidiomycete, share extensive regions of synteny. The largest syntenic blocks occur in regions with low meiotic recombination rates, no transposable elements, and tight gene spacing, where orthologous single-copy genes are overrepresented. The chromosome assembly of C. cinerea is an essential resource in understanding the evolution of multicellularity in the fungi.
Published: 2010
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9. Periplasmic Proteins of the Extremophile Acidithiobacillus ferrooxidans

Author: Carlos A. Jerez, Aaron J. Mackey, Donald F. Hunt, Simon Beard, Lissette Valenzuela, An Chi, and Jeffrey Shabanowitz
Subjects: biology, Biomining, Periplasmic space, Acidithiobacillus, biology.organism_classification, Proteomics, medicine.disease_cause, Biochemistry, Analytical Chemistry, Acidophile, medicine, Cell envelope, Bacterial outer membrane, Molecular Biology, Escherichia coli
Abstract: Acidithiobacillus ferrooxidans is a chemolithoautotrophic acidophile capable of obtaining energy by oxidizing ferrous iron or sulfur compounds such as metal sulfides. Some of the proteins involved in these oxidations have been described as forming part of the periplasm of this extremophile. The detailed study of the periplasmic components constitutes an important area to understand the physiology and environmental interactions of microorganisms. Proteomics analysis of the periplasmic fraction of A. ferrooxidans ATCC 23270 was performed by using high resolution linear ion trap-FT MS. We identified a total of 131 proteins in the periplasm of the microorganism grown in thiosulfate. When possible, functional categories were assigned to the proteins: 13.8% were transport and binding proteins, 14.6% were several kinds of cell envelope proteins, 10.8% were involved in energy metabolism, 10% were related to protein fate and folding, 10% were proteins with unknown functions, and 26.1% were proteins without homologues in databases. These last proteins are most likely characteristic of A. ferrooxidans and may have important roles yet to be assigned. The majority of the periplasmic proteins from A. ferrooxidans were very basic compared with those of neutrophilic microorganisms such as Escherichia coli, suggesting a special adaptation of the chemolithoautotrophic bacterium to its very acidic environment. The high throughput proteomics approach used here not only helps to understand the physiology of this extreme acidophile but also offers an important contribution to the functional annotation for the available genomes of biomining microorganisms such as A. ferrooxidans for which no efficient genetic systems are available to disrupt genes by procedures such as homologous recombination.
Published: 2007
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10. Transcriptional Profiling Suggests That Nevirapine and Ritonavir Cause Drug Induced Liver Injury Through Distinct Mechanisms in Primary Human Hepatocytes

Author: Ajit Dash, Charles W. Qualls, Mark J. Lawson, Svetlana Marukian, David Manka, Ylva Terelius, Maria Sol Collado, Robert A. Figler, Aaron J. Mackey, Brian R. Wamhoff, and Brett R. Blackman
Subjects: 0301 basic medicine, Drug, Nevirapine, CYP2B6, Anti-HIV Agents, media_common.quotation_subject, Cell Culture Techniques, Drug Evaluation, Preclinical, Pharmacology, Biology, Toxicology, Article, 03 medical and health sciences, 0302 clinical medicine, In vivo, medicine, Humans, Cells, Cultured, media_common, Ritonavir, CYP3A4, Reverse-transcriptase inhibitor, General Medicine, 030104 developmental biology, Liver, 030220 oncology & carcinogenesis, Toxicity, Hepatocytes, Chemical and Drug Induced Liver Injury, Transcriptome, medicine.drug
Abstract: Drug induced liver injury (DILI), a major cause of pre- and post-approval failure, is challenging to predict pre-clinically. Predicting DILI is challenging due to varied underlying direct and indirect mechanisms. Nevirapine, a non-nucleoside reverse transcriptase inhibitor (NNRTI) and Ritonavir, a protease inhibitor, are antiviral drugs that cause clinical DILI with different phenotypes via different mechanisms. Assessing DILI in vitro in hepatocyte cultures typically requires drug exposures significantly higher than clinical plasma Cmax concentrations, making clinical interpretations of mechanistic pathway changes challenging. We previously described a system that uses liver-derived hemodynamic blood flow and transport parameters to restore primary human hepatocyte biology, and drug responses at concentrations relevant to in vivo or clinical exposure levels. Using this system, primary hepatocytes from 5 human donors were exposed to approximating clinical therapeutic and supra-therapeutic concentrations levels of Nevirapine (11.3 and 175.0 μM) and Ritonavir (3.5 and 62.4 μM) for 48 hours. Whole genome transcriptomics was performed by RNAseq along with functional assays for metabolic activity and function. We observed effects at both doses, but a greater number of genes were differentially expressed with higher probability at the toxic concentrations. At the toxic doses, both drugs showed direct cholestatic potential with Nevirapine increasing bile synthesis and Ritonavir inhibiting bile acid transport. Clear differences in antigen presentation were noted, with marked activation of MHC Class I by Nevirapine and suppression by Ritonavir. This suggests CD8+ T cell involvement for Nevirapine and possibly NK Killer cells for Ritonavir. Both compounds induced several drug metabolizing genes (including CYP2B6, CYP3A4 and UGT1A1), mediated by CAR activation in Nevirapine and PXR in Ritonavir. Unlike Ritonavir, Nevirapine did not increase fatty acid synthesis or activate the respiratory electron chain with simultaneous mitochondrial uncoupling supporting clinical reports of a lower propensity for steatosis. This in vitro study offers insights into the disparate direct and immune mediated toxicity mechanisms underlying Nevirapine and Ritonavir toxicity in the clinic.
Published: 2015

11. An In Vitro Cynomolgus Vascular Surrogate System for Preclinical Drug Assessment and Human Translation

Author: David Manka, Ryan E. Feaver, Mark J. Lawson, Brian R. Wamhoff, Charles W. Qualls, Banumathi K. Cole, Erica Berzin, Aaron J. Mackey, Andrew W. Pryor, Robert A. Figler, James R. Turk, Michael B. Simmers, M. Sol Collado, and Brett R. Blackman
Subjects: Drug, Statin, medicine.drug_class, media_common.quotation_subject, Drug Evaluation, Preclinical, Inflammation, Pharmacology, Biology, In Vitro Techniques, Muscle, Smooth, Vascular, Species Specificity, In vivo, medicine, Animals, Humans, Cells, Cultured, media_common, Models, Cardiovascular, Endothelial Cells, Cell cycle, In vitro, Lipoproteins, LDL, Macaca fascicularis, Mechanism of action, Drug development, Cardiovascular Diseases, medicine.symptom, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Cardiology and Cardiovascular Medicine
Abstract: Objectives— The predictive value of animal and in vitro systems for drug development is limited, particularly for nonhuman primate studies as it is difficult to deduce the drug mechanism of action. We describe the development of an in vitro cynomolgus macaque vascular system that reflects the in vivo biology of healthy, atheroprone, or advanced inflammatory cardiovascular disease conditions. Approach and Results— We compare the responses of the in vitro human and cynomolgus vascular systems to 4 statins. Although statins exert beneficial pleiotropic effects on the human vasculature, the mechanism of action is difficult to investigate at the tissue level. Using RNA sequencing, we quantified the response to statins and report that most statins significantly increased the expression of genes that promote vascular health while suppressing inflammatory cytokine gene expression. Applying computational pathway analytics, we identified statin-regulated biological themes, independent of cholesterol lowering, that provide mechanisms for off-target effects, including thrombosis, cell cycle regulation, glycogen metabolism, and ethanol degradation. Conclusions— The cynomolgus vascular system described herein mimics the baseline and inflammatory regional biology of the human vasculature, including statin responsiveness, and provides mechanistic insight not achievable in vivo.
Published: 2015

12. OrthoMCL-DB: querying a comprehensive multi-species collection of ortholog groups

Author: Christian J. Stoeckert, David S. Roos, Feng Chen, and Aaron J. Mackey
Subjects: 0106 biological sciences, Proteome, Architecture domain, media_common.quotation_subject, Genomics, Computational biology, Biology, 01 natural sciences, Genome, Article, User-Computer Interface, 03 medical and health sciences, Phylogenetics, Genetics, Animals, Cluster Analysis, Databases, Protein, Phyletic gradualism, Phylogeny, 030304 developmental biology, media_common, Whole genome sequencing, Internet, 0303 health sciences, Multiple sequence alignment, Sequence Homology, Amino Acid, 010606 plant biology & botany
Abstract: The OrthoMCL database (http://orthomcl.cbil.upenn.edu) houses ortholog group predictions for 55 species, including 16 bacterial and 4 archaeal genomes representing phylogenetically diverse lineages, and most currently available complete eukaryotic genomes: 24 unikonts (12 animals, 9 fungi, microsporidium, Dictyostelium, Entamoeba), 4 plants/algae and 7 apicomplexan parasites. OrthoMCL software was used to cluster proteins based on sequence similarity, using an all-against-all BLAST search of each species' proteome, followed by normalization of inter-species differences, and Markov clustering. A total of 511,797 proteins (81.6% of the total dataset) were clustered into 70,388 ortholog groups. The ortholog database may be queried based on protein or group accession numbers, keyword descriptions or BLAST similarity. Ortholog groups exhibiting specific phyletic patterns may also be identified, using either a graphical interface or a text-based Phyletic Pattern Expression grammar. Information for ortholog groups includes the phyletic profile, the list of member proteins and a multiple sequence alignment, a statistical summary and graphical view of similarities, and a graphical representation of domain architecture. OrthoMCL software, the entire FASTA dataset employed and clustering results are available for download. OrthoMCL-DB provides a centralized warehouse for orthology prediction among multiple species, and will be updated and expanded as additional genome sequence data become available.
Published: 2006
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13. The genome of Cryptosporidium hominis

Author: William R. Pearson, Donna E. Akiyoshi, Gregory A. Buck, Giovanni Widmer, Paul H. Dear, Luiz S. Ozaki, Myrna G. Serrano, Ping Xu, Patricio Manque, Vivek Kapur, Mitchell S. Abrahamsen, João M. P. Alves, Aaron J. Mackey, Darrell L. Peterson, Saul Tzipori, Daniela Puiu, Yingping Wang, and Alan T. Bankier
Subjects: animal diseases, Genes, Protozoan, Protozoan Proteins, Cryptosporidium, Biology, Genome, Chromosomes, Microbiology, Evolution, Molecular, Apicomplexa, parasitic diseases, Animals, Humans, Gene, Cryptosporidium parvum, Genetics, Apicoplast, Multidisciplinary, Intermediate host, Genomics, biology.organism_classification, Enzymes, Phenotype, Apical complex, Genome, Protozoan, Cryptosporidium hominis
Abstract: Cryptosporidium species cause acute gastroenteritis and diarrhoea worldwide. They are members of the Apicomplexa--protozoan pathogens that invade host cells by using a specialized apical complex and are usually transmitted by an invertebrate vector or intermediate host. In contrast to other Apicomplexans, Cryptosporidium is transmitted by ingestion of oocysts and completes its life cycle in a single host. No therapy is available, and control focuses on eliminating oocysts in water supplies. Two species, C. hominis and C. parvum, which differ in host range, genotype and pathogenicity, are most relevant to humans. C. hominis is restricted to humans, whereas C. parvum also infects other mammals. Here we describe the eight-chromosome approximately 9.2-million-base genome of C. hominis. The complement of C. hominis protein-coding genes shows a striking concordance with the requirements imposed by the environmental niches the parasite inhabits. Energy metabolism is largely from glycolysis. Both aerobic and anaerobic metabolisms are available, the former requiring an alternative electron transport system in a simplified mitochondrion. Biosynthesis capabilities are limited, explaining an extensive array of transporters. Evidence of an apicoplast is absent, but genes associated with apical complex organelles are present. C. hominis and C. parvum exhibit very similar gene complements, and phenotypic differences between these parasites must be due to subtle sequence divergence.
Published: 2004
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14. Entamoeba histolytica: sequence conservation of the Gal/GalNAc lectin from clinical isolates

Author: William A. Petri, David L. Beck, Brendan J. Loftus, Nino Trapaidze, William R. Pearson, Aaron J. Mackey, Rashidul Haque, and Mehmet Tanyuksel
Subjects: Male, Protein subunit, Molecular Sequence Data, Immunology, Protozoan Proteins, Virulence, Georgia (Republic), Polymerase Chain Reaction, Microbiology, Entamoeba histolytica, Lectins, parasitic diseases, Animals, Humans, Amino Acid Sequence, Genetic variability, Child, Gene, Conserved Sequence, Genetics, Bangladesh, Entamoebiasis, biology, Genetic Variation, Lectin, General Medicine, Middle Aged, biology.organism_classification, Infectious Diseases, Interaction with host, Child, Preschool, biology.protein, Protozoa, Female, Parasitology
Abstract: The Gal/GalNAc lectin gene of Entamoeba histolytica is a major amebic virulence protein responsible for interaction with host tissues. We investigated sequence differences in the Gal/GalNAc lectin heavy subunit in three isolates from Bangladesh and one isolate from Georgia, each of which was determined to be genetically distinct by SREHP AluI digestion. Interestingly, we observed only slight genetic diversity in the lectin gene as compared with the HM1:IMSS laboratory strain, originally a clinical isolate from Mexico. Genetic conservation of the Gal/GalNAc lectin between isolates may reflect that the lectin is under strong functional selection or possibly, that E. histolytica is a clonal population. Sequence conservation of the lectin indicates that immune responses against it should be cross-protective.
Published: 2002
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15. BAYSIC: a Bayesian method for combining sets of genome variants with improved specificity and sensitivity

Author: Daniel Weaver, Justin T. Reese, Brandi L. Cantarel, Jianhua Zhang, Aaron J. Mackey, and Nathan McNeill
Subjects: Genome variants, Bayesian probability, Posterior probability, SNP, Single-nucleotide polymorphism, Computational biology, Biology, computer.software_genre, Bayesian, Polymorphism, Single Nucleotide, Biochemistry, Genome, 03 medical and health sciences, Bayes' theorem, 0302 clinical medicine, Software Design, Structural Biology, Latent class analysis, Humans, Exome, 1000 Genomes Project, Molecular Biology, Cancer, Probability, 030304 developmental biology, 0303 health sciences, Genome, Human, Somatic mutation, Applied Mathematics, Bayes Theorem, 3. Good health, Computer Science Applications, 030220 oncology & carcinogenesis, Mutation, Human genome, Data mining, computer, Software, Algorithms
Abstract: Background Accurate genomic variant detection is an essential step in gleaning medically useful information from genome data. However, low concordance among variant-calling methods reduces confidence in the clinical validity of whole genome and exome sequence data, and confounds downstream analysis for applications in genome medicine. Here we describe BAYSIC (BAYeSian Integrated Caller), which combines SNP variant calls produced by different methods (e.g. GATK, FreeBayes, Atlas, SamTools, etc.) into a more accurate set of variant calls. BAYSIC differs from majority voting, consensus or other ad hoc intersection-based schemes for combining sets of genome variant calls. Unlike other classification methods, the underlying BAYSIC model does not require training using a “gold standard” of true positives. Rather, with each new dataset, BAYSIC performs an unsupervised, fully Bayesian latent class analysis to estimate false positive and false negative error rates for each input method. The user specifies a posterior probability threshold according to the user’s tolerance for false positive and false negative errors; lowering the posterior probability threshold allows the user to trade specificity for sensitivity while raising the threshold increases specificity in exchange for sensitivity. Results We assessed the performance of BAYSIC in comparison to other variant detection methods using ten low coverage (~5X) samples from The 1000 Genomes Project, a tumor/normal exome pair (40X), and exome sequences (40X) from positive control samples previously identified to contain clinically relevant SNPs. We demonstrated BAYSIC’s superior variant-calling accuracy, both for somatic mutation detection and germline variant detection. Conclusions BAYSIC provides a method for combining sets of SNP variant calls produced by different variant calling programs. The integrated set of SNP variant calls produced by BAYSIC improves the sensitivity and specificity of the variant calls used as input. In addition to combining sets of germline variants, BAYSIC can also be used to combine sets of somatic mutations detected in the context of tumor/normal sequencing experiments.
Published: 2014
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16. Whole-exome sequencing identifies rare and low-frequency coding variants associated with LDL cholesterol

Author: Leslie A. Lange, Youna Hu, He Zhang, Chenyi Xue, Ellen M. Schmidt, Zheng-Zheng Tang, Chris Bizon, Ethan M. Lange, Joshua D. Smith, Emily H. Turner, Goo Jun, Hyun Min Kang, Gina Peloso, Paul Auer, Kuo-ping Li, Jason Flannick, Ji Zhang, Christian Fuchsberger, Kyle Gaulton, Cecilia Lindgren, Adam Locke, Alisa Manning, Xueling Sim, Manuel A. Rivas, Oddgeir L. Holmen, Omri Gottesman, Yingchang Lu, Douglas Ruderfer, Eli A. Stahl, Qing Duan, Yun Li, Peter Durda, Shuo Jiao, Aaron Isaacs, Albert Hofman, Joshua C. Bis, Adolfo Correa, Michael E. Griswold, Johanna Jakobsdottir, Albert V. Smith, Pamela J. Schreiner, Mary F. Feitosa, Qunyuan Zhang, Jennifer E. Huffman, Jacy Crosby, Christina L. Wassel, Ron Do, Nora Franceschini, Lisa W. Martin, Jennifer G. Robinson, Themistocles L. Assimes, David R. Crosslin, Elisabeth A. Rosenthal, Michael Tsai, Mark J. Rieder, Deborah N. Farlow, Aaron R. Folsom, Thomas Lumley, Ervin R. Fox, Christopher S. Carlson, Ulrike Peters, Rebecca D. Jackson, Cornelia M. van Duijn, André G. Uitterlinden, Daniel Levy, Jerome I. Rotter, Herman A. Taylor, Vilmundur Gudnason, David S. Siscovick, Myriam Fornage, Ingrid B. Borecki, Caroline Hayward, Igor Rudan, Y. Eugene Chen, Erwin P. Bottinger, Ruth J.F. Loos, Pål Sætrom, Kristian Hveem, Michael Boehnke, Leif Groop, Mark McCarthy, Thomas Meitinger, Christie M. Ballantyne, Stacey B. Gabriel, Christopher J. O’Donnell, Wendy S. Post, Kari E. North, Alexander P. Reiner, Eric Boerwinkle, Bruce M. Psaty, David Altshuler, Sekar Kathiresan, Dan-Yu Lin, Gail P. Jarvik, L. Adrienne Cupples, Charles Kooperberg, James G. Wilson, Deborah A. Nickerson, Goncalo R. Abecasis, Stephen S. Rich, Russell P. Tracy, Cristen J. Willer, David M. Altshuler, Gonçalo R. Abecasis, Hooman Allayee, Sharon Cresci, Mark J. Daly, Paul I.W. de Bakker, Mark A. DePristo, Peter Donnelly, Tim Fennell, Kiran Garimella, Stanley L. Hazen, Daniel M. Jordan, Adam Kiezun, Guillaume Lettre, Bingshan Li, Mingyao Li, Christopher H. Newton-Cheh, Sandosh Padmanabhan, Sara Pulit, Daniel J. Rader, David Reich, Muredach P. Reilly, Steve Schwartz, Laura Scott, John A. Spertus, Nathaniel O. Stitziel, Nina Stoletzki, Shamil R. Sunyaev, Benjamin F. Voight, Ermeg Akylbekova, Larry D. Atwood, Maja Barbalic, R. Graham Barr, Emelia J. Benjamin, Joshua Bis, Donald W. Bowden, Jennifer Brody, Matthew Budoff, Greg Burke, Sarah Buxbaum, Jeff Carr, Donna T. Chen, Ida Y. Chen, Wei-Min Chen, Pat Concannon, Ralph D’Agostino, Anita L. DeStefano, Albert Dreisbach, Josée Dupuis, J. Peter Durda, Jaclyn Ellis, Caroline S. Fox, Ervin Fox, Vincent Funari, Santhi K. Ganesh, Julius Gardin, David Goff, Ora Gordon, Wayne Grody, Myron Gross, Xiuqing Guo, Ira M. Hall, Nancy L. Heard-Costa, Susan R. Heckbert, Nicholas Heintz, David M. Herrington, DeMarc Hickson, Jie Huang, Shih-Jen Hwang, David R. Jacobs, Nancy S. Jenny, Andrew D. Johnson, Craig W. Johnson, Steven Kawut, Richard Kronmal, Raluca Kurz, Martin G. Larson, Mark Lawson, Cora E. Lewis, Dalin Li, Honghuang Lin, Chunyu Liu, Jiankang Liu, Kiang Liu, Xiaoming Liu, Yongmei Liu, William T. Longstreth, Cay Loria, Kathryn Lunetta, Aaron J. Mackey, Rachel Mackey, Ani Manichaikul, Taylor Maxwell, Barbara McKnight, James B. Meigs, Alanna C. Morrison, Solomon K. Musani, Josyf C. Mychaleckyj, Jennifer A. Nettleton, Kari North, Daniel O’Leary, Frank Ong, Walter Palmas, James S. Pankow, Nathan D. Pankratz, Shom Paul, Marco Perez, Sharina D. Person, Joseph Polak, Aaron R. Quinlan, Leslie J. Raffel, Vasan S. Ramachandran, Kenneth Rice, Jill P. Sanders, Pamela Schreiner, Sudha Seshadri, Steve Shea, Stephen Sidney, Kevin Silverstein, Nicholas L. Smith, Nona Sotoodehnia, Asoke Srinivasan, Kent Taylor, Fridtjof Thomas, Michael Y. Tsai, Kelly A. Volcik, Chrstina L. Wassel, Karol Watson, Gina Wei, Wendy White, Kerri L. Wiggins, Jemma B. Wilk, O. Dale Williams, Gregory Wilson, Phillip Wolf, Neil A. Zakai, John Hardy, James F. Meschia, Michael Nalls, Andrew Singleton, Brad Worrall, Michael J. Bamshad, Kathleen C. Barnes, Ibrahim Abdulhamid, Frank Accurso, Ran Anbar, Terri Beaty, Abigail Bigham, Phillip Black, Eugene Bleecker, Kati Buckingham, Anne Marie Cairns, Daniel Caplan, Barbara Chatfield, Aaron Chidekel, Michael Cho, David C. Christiani, James D. Crapo, Julia Crouch, Denise Daley, Anthony Dang, Hong Dang, Alicia De Paula, Joan DeCelie-Germana, Allen DozorMitch Drumm, Maynard Dyson, Julia Emerson, Mary J. Emond, Thomas Ferkol, Robert Fink, Cassandra Foster, Deborah Froh, Li Gao, William Gershan, Ronald L. Gibson, Elizabeth Godwin, Magdalen Gondor, Hector Gutierrez, Nadia N. Hansel, Paul M. Hassoun, Peter Hiatt, John E. Hokanson, Michelle Howenstine, Laura K. Hummer, Jamshed Kanga, Yoonhee Kim, Michael R. Knowles, Michael Konstan, Thomas Lahiri, Nan Laird, Christoph Lange, Lin Lin, Xihong Lin, Tin L. Louie, David Lynch, Barry Make, Thomas R. Martin, Steve C. Mathai, Rasika A. Mathias, John McNamara, Sharon McNamara, Deborah Meyers, Susan Millard, Peter Mogayzel, Richard Moss, Tanda Murray, Dennis Nielson, Blakeslee Noyes, Wanda O’Neal, David Orenstein, Brian O’Sullivan, Rhonda Pace, Peter Pare, H. Worth Parker, Mary Ann Passero, Elizabeth Perkett, Adrienne Prestridge, Nicholas M. Rafaels, Bonnie Ramsey, Elizabeth Regan, Clement Ren, George Retsch-Bogart, Michael Rock, Antony Rosen, Margaret Rosenfeld, Ingo Ruczinski, Andrew Sanford, David Schaeffer, Cindy Sell, Daniel Sheehan, Edwin K. Silverman, Don Sin, Terry Spencer, Jackie Stonebraker, Holly K. Tabor, Laurie Varlotta, Candelaria I. Vergara, Robert Weiss, Fred Wigley, Robert A. Wise, Fred A. Wright, Mark M. Wurfel, Robert Zanni, Fei Zou, Phil Green, Jay Shendure, Joshua M. Akey, Carlos D. Bustamante, Evan E. Eichler, P. Keolu Fox, Wenqing Fu, Adam Gordon, Simon Gravel, Jill M. Johnsen, Mengyuan Kan, Eimear E. Kenny, Jeffrey M. Kidd, Fremiet Lara-Garduno, Suzanne M. Leal, Dajiang J. Liu, Sean McGee, Timothy D. O’Connor, Bryan Paeper, Peggy D. Robertson, Jeffrey C. Staples, Jacob A. Tennessen, Gao Wang, Qian Yi, Rebecca Jackson, Garnet Anderson, Hoda Anton-Culver, Paul L. Auer, Shirley Beresford, Henry Black, Robert Brunner, Robert Brzyski, Dale Burwen, Bette Caan, Cara L. Carty, Rowan Chlebowski, Steven Cummings, J. David Curb, Charles B. Eaton, Leslie Ford, Stephanie M. Fullerton, Margery Gass, Nancy Geller, Gerardo Heiss, Barbara V. Howard, Li Hsu, Carolyn M. Hutter, John Ioannidis, Karen C. Johnson, Lewis Kuller, Andrea LaCroix, Kamakshi Lakshminarayan, Dorothy Lane, Norman Lasser, Erin LeBlanc, Kuo-Ping Li, Marian Limacher, Benjamin A. Logsdon, Shari Ludlam, JoAnn E. Manson, Karen Margolis, Lisa Martin, Joan McGowan, Keri L. Monda, Jane Morley Kotchen, Lauren Nathan, Judith Ockene, Mary Jo O’Sullivan, Lawrence S. Phillips, Ross L. Prentice, John Robbins, Jacques E. Rossouw, Haleh Sangi-Haghpeykar, Gloria E. Sarto, Sally Shumaker, Michael S. Simon, Marcia L. Stefanick, Evan Stein, Hua Tang, Kira C. Taylor, Cynthia A. Thomson, Timothy A. Thornton, Linda Van Horn, Mara Vitolins, Jean Wactawski-Wende, Robert Wallace, Sylvia Wassertheil-Smoller, Donglin Zeng, Deborah Applebaum-Bowden, Michael Feolo, Weiniu Gan, Dina N. Paltoo, Phyliss Sholinsky, Anne Sturcke, Epidemiology, and Internal Medicine
Subjects: Male, Genome-wide association study, 030204 cardiovascular system & hematology, Cohort Studies, 0302 clinical medicine, Gene Frequency, Receptors, Genotype, Dyslipidemias/blood, Receptors, LDL/genetics, Genetics(clinical), Exome, Genetics (clinical), Exome sequencing, Genetics, 0303 health sciences, Serine Endopeptidases, Single Nucleotide, Middle Aged, 3. Good health, Cholesterol, Phenotype, Genetic Code, Cholesterol, LDL/genetics, Female, lipids (amino acids, peptides, and proteins), Proprotein Convertases, Proprotein Convertase 9, Sequence Analysis, Adult, Apolipoproteins E/blood, LDL/genetics, Serine Endopeptidases/genetics, Single-nucleotide polymorphism, Biology, Polymorphism, Single Nucleotide, Article, 03 medical and health sciences, Apolipoproteins E, SDG 3 - Good Health and Well-being, Humans, Polymorphism, Allele frequency, 030304 developmental biology, Genetic association, Aged, Dyslipidemias, PCSK9, DNA, Cholesterol, LDL, Lipase, Sequence Analysis, DNA, Receptors, LDL, Lipase/genetics, Proprotein Convertases/genetics, Follow-Up Studies, Genome-Wide Association Study
Abstract: Elevated low-density lipoprotein cholesterol (LDL-C) is a treatable, heritable risk factor for cardiovascular disease. Genome-wide association studies (GWASs) have identified 157 variants associated with lipid levels but are not well suited to assess the impact of rare and low-frequency variants. To determine whether rare or low-frequency coding variants are associated with LDL-C, we exome sequenced 2,005 individuals, including 554 individuals selected for extreme LDL-C (>98(th) or
Published: 2014

17. Genetic mapping of a murine locus controlling development of T helper 1/T helper 2 type responses

Author: William F. Dietrich, Robert G. Steen, Kathy Frederick, James D. Gorham, Mehmet L. Guler, Aaron J. Mackey, Kenneth M. Murphy, and Mark J. Daly
Subjects: Male, T cell, Locus (genetics), Biology, Chromosomes, Mice, Th2 Cells, Gene mapping, Antigen, medicine, Animals, Humans, Genetics, Mice, Inbred BALB C, Multidisciplinary, Chromosome Mapping, Chromosome, Th1 Cells, Interleukin-12, Phenotype, Molecular biology, medicine.anatomical_structure, Chromosomal region, Interleukin 12, Chromosomes, Human, Pair 5, Female, Polymorphism, Restriction Fragment Length, Research Article
Abstract: Genetic background of the T cell can influence T helper (Th) phenotype development, with some murine strains (e.g., B10.D2) favoring Th1 development and others (e.g., BALB/c) favoring Th2 development. Recently we found that B10.D2 exhibit an intrinsically greater capacity to maintain interleukin 12 (IL-12) responsiveness under neutral conditions in vitro compared with BALB/c T cells, allowing for prolonged capacity to undergo IL-12-induced Th1 development. To begin identification of the loci controlling this genetic effect, we used a T-cell antigen receptor-transgenic system for in vitro analysis of intercrosses between BALB/c and B10.D2 mice and have identified a locus on murine chromosome 11 that controls the maintenance of IL-12 responsiveness, and therefore the subsequent Th1/Th2 response. This chromosomal region is syntenic with a locus on human chromosome 5q31.1 shown to be associated with elevated serum IgE levels, suggesting that genetic control of Th1/Th2 differentiation in mouse, and of atopy development in humans, may be expressed through similar mechanisms.
Published: 1996
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18. CRP: Cleavage of Radiolabeled Phosphoproteins

Author: Timothy A.J. Haystead, William R. Pearson, and Aaron J. Mackey
Subjects: Internet, Edman degradation, Articles, Biology, Phosphoproteins, Cleavage (embryo), Molecular biology, Serine, Phosphoserine, Phosphothreonine, Biochemistry, Sequence Analysis, Protein, Phosphoprotein, Genetics, Humans, Phosphorylation, Protein phosphorylation, Radioactive Tracers, Threonine, Phosphotyrosine, Peptide sequence, Software
Abstract: The CRP (Cleavage of Radiolabeled Phosphoproteins) program guides the design and interpretation of experiments to identify protein phosphorylation sites by Edman sequencing of unseparated peptides. Traditionally, phosphorylation sites are determined by cleaving the phosphoprotein and separating the peptides for Edman 32 P-phosphate release sequencing. CRP analysis of a phosphoprotein’s sequence accelerates this process by omitting the separation step: given a protein sequence of interest, the CRP program performs an in silico proteolytic cleavage of the sequence and reports the predicted Edman cycles in which radioactivity would be observed if a given serine, threonine or tyrosine were phosphorylated. Experimentally observed cycles containing 32 P can be compared with CRP predictions to confirm candidate sites and/or explore the ability of additional cleavage experiments to resolve remaining ambiguities. To reduce ambiguity, the phosphorylated residue (P-Tyr, P-Ser or P-Thr) can be determined experimentally, and CRP will ignore sites with alternative residues. CRP also provides simple predictions of likely phosphorylation sites using known kinase recognition motifs. The CRP interface is available at http://fasta.bioch.virginia.edu/crp.
Published: 2003
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19. Global changes in gene expression during compatible and incompatible interactions of cowpea (Vigna unguiculata L.) with the root parasitic angiosperm Striga gesnerioides

Author: Mark J. Lawson, Shom N. Paul, Aaron J. Mackey, Michael P. Timko, Karolina E. Mellor, and Kan Huang
Subjects: 0106 biological sciences, lcsh:QH426-470, lcsh:Biotechnology, Resistance, Striga, Genes, Plant, 01 natural sciences, Plant Roots, Host-Parasite Interactions, Vigna, 03 medical and health sciences, Symbiosis, Gene Expression Regulation, Plant, lcsh:TP248.13-248.65, Botany, Gene expression, Genetics, Defense, Gene, 030304 developmental biology, Plant Diseases, 2. Zero hunger, 0303 health sciences, biology, Abiotic stress, Striga gesnerioides, food and beverages, Fabaceae, biology.organism_classification, Witchweed, lcsh:Genetics, Cowpea, Vigna unguiculata, DNA microarray, Secondary cell wall, Transcription, 010606 plant biology & botany, Research Article, Biotechnology
Abstract: Background Cowpea, Vigna unguiculata L. Walp., is one of the most important food and forage legumes in the semi-arid tropics. While most domesticated forms of cowpea are susceptible to the root parasitic weed Striga gesnerioides, several cultivars have been identified that show race-specific resistance. Cowpea cultivar B301 contains the RSG3-301 gene for resistance to S. gesnerioides race SG3, but is susceptible to race SG4z. When challenged by SG3, roots of cultivar B301 develop a strong resistance response characterized by a hypersensitive reaction and cell death at the site of parasite attachment. In contrast, no visible response occurs in B301 roots parasitized by SG4z. Results Gene expression in the roots of the cowpea cultivar B301 during compatible (susceptible) and incompatible (resistant) interactions with S. gesnerioides races SG4z and SG3, respectively, were investigated at the early (6 days post-inoculation (dpi)) and late (13 dpi) stages of the resistance response using a Nimblegen custom design cowpea microarray. A total of 111 genes were differentially expressed in B301 roots at 6 dpi; this number increased to 2102 genes at 13 dpi. At 13 dpi, a total of 1944 genes were differentially expressed during compatible (susceptible) interactions of B301 with SG4z. Genes and pathways involved in signal transduction, programmed cell death and apoptosis, and defense response to biotic and abiotic stress were differentially expressed in the early resistance response; at the later time point, enrichment was primarily for defense-related gene expression, and genes encoding components of lignifications and secondary wall formation. In compatible interactions (B301 – SG4z), multiple defense pathways were repressed, including those involved in lignin biosynthesis and secondary cell wall modifications, while cellular transport processes for nitrogen and sulfur were increased. Conclusion Distinct changes in global gene expression profiles occur in host roots following successful and unsuccessful attempted parasitism by Striga. Induction of specific defense related genes and pathways defines components of a unique resistance mechanism. Some genes and pathways up-regulated in the host resistance response to SG3 are repressed in the susceptible interactions, suggesting that the parasite is targeting specific components of the host’s defense. These results add to our understanding of plant-parasite interactions and the evolution of resistance to parasitic weeds.
Published: 2012

20. The Drosophila melanogaster Genetic Reference Panel

Author: Sandra L. Lee, Richard A. Gibbs, Julio Rozas, Mehwish Javaid, Zeke Harris, Pablo Librado, Robert R. H. Anholt, Kevin R. Thornton, Stephanie M. Rollmann, Julie M. Cridland, Jeffrey G. Reid, Shalini N. Jhangiani, Lesley S. Chaboub, Maite G. Barrón, Akihiko Yamamoto, Stephen Richards, Julien F. Ayroles, Mark F. Richardson, Miquel Ràmia, Dianhui Zhu, Lora Perales, Yi Han, David Castellano, Joy Jayaseelan, David Mittelman, Casey M. Bergman, Kerstin P. Blankenburg, Carson Qu, Yiming Zhu, Lynne V. Nazareth, Irene Newsham, Raquel S. Linheiro, Aaron J. Mackey, Crystal Bess, Faye Lawrence, Mary Anna Carbone, Yuanqing Wu, Fremiet Lara, Lavanya Turlapati, Trudy F. C. Mackay, Antonio Barbadilla, Nehad Saada, Sònia Casillas, Eric A. Stone, Donna M. Muzny, Kim C. Worley, Mala Munidasa, Ling-Ling Pu, Laura H Duncan, Richard F. Lyman, Michael M. Magwire, and Katherine W. Jordan
Subjects: 0106 biological sciences, Genome-wide association study, 01 natural sciences, Genome-wide association studies, Population genomics, 2.1 Biological and endogenous factors, Aetiology, Genetics, 0303 health sciences, education.field_of_study, Multidisciplinary, Genomics, Single Nucleotide, Telomere, Drosophila melanogaster, Phenotype, Drosophila, Biotechnology, X Chromosome, Molecular Genetic Variation, Genotype, General Science & Technology, Population, Centromere, Quantitative Trait Loci, Biology, Quantitative trait locus, 010603 evolutionary biology, Polymorphism, Single Nucleotide, Article, Chromosomes, 03 medical and health sciences, Genetic, Animals, Genetic variation, Allele, Selection, Genetic, Polymorphism, education, Selection, Alleles, 030304 developmental biology, Human Genome, biology.organism_classification, Chromosomes, Insect, Evolutionary biology, Starvation, Generic health relevance, Insect, Genome-Wide Association Study
Abstract: A major challenge of biology is understanding the relationship between molecular genetic variation and variation in quantitative traits, including fitness. This relationship determines our ability to predict phenotypes from genotypes and to understand how evolutionary forces shape variation within and between species. Previous efforts to dissect the genotype-phenotype map were based on incomplete genotypic information. Here, we describe the Drosophila melanogaster Genetic Reference Panel (DGRP), a community resource for analysis of population genomics and quantitative traits. The DGRP consists of fully sequenced inbred lines derived from a natural population. Population genomic analyses reveal reduced polymorphism in centromeric autosomal regions and the X chromosome, evidence for positive and negative selection, and rapid evolution of the X chromosome. Many variants in novel genes, most at low frequency, are associated with quantitative traits and explain a large fraction of the phenotypic variance. The DGRP facilitates genotype-phenotype mapping using the power of Drosophila genetics.
Published: 2012

21. Evigan: a hidden variable model for integrating gene evidence for eukaryotic gene prediction

Author: Qian Liu, David S. Roos, Fernando Pereira, and Aaron J. Mackey
Subjects: Statistics and Probability, Candidate gene, Computational biology, Biology, computer.software_genre, ENCODE, Biochemistry, Genome, Annotation, Automation, Databases, Genetic, Animals, Humans, Molecular Biology, Gene, Likelihood Functions, Models, Genetic, Bayesian network, Gene Annotation, Computer Science Applications, Computational Mathematics, Computational Theory and Mathematics, Database Management Systems, Human genome, Data mining, computer
Abstract: Motivation: The increasing diversity and variable quality of evidence relevant to gene annotation argues for a probabilistic framework that automatically integrates such evidence to yield candidate gene models. Results: Evigan is an automated gene annotation program for eukaryotic genomes, employing probabilistic inference to integrate multiple sources of gene evidence. The probabilistic model is a dynamic Bayes network whose parameters are adjusted to maximize the probability of observed evidence. Consensus gene predictions are then derived by maximum likelihood decoding, yielding n-best models (with probabilities for each). Evigan is capable of accommodating a variety of evidence types, including (but not limited to) gene models computed by diverse gene finders, BLAST hits, EST matches, and splice site predictions; learned parameters encode the relative quality of evidence sources. Since separate training data are not required (apart from the training sets used by individual gene finders), Evigan is particularly attractive for newly sequenced genomes where little or no reliable manually curated annotation is available. The ability to produce a ranked list of alternative gene models may facilitate identification of alternatively spliced transcripts. Experimental application to ENCODE regions of the human genome, and the genomes of Plasmodium vivax and Arabidopsis thaliana show that Evigan achieves better performance than any of the individual data sources used as evidence. Availability: The source code is available at http://www.seas.upenn.edu/~strctlrn/evigan/evigan.html Contact: qianliu@seas.upenn.edu
Published: 2008

22. ToxoDB: an integrated Toxoplasma gondii database resource

Author: Christian J. Stoeckert, Amit Bahl, Brian P. Brunk, John Iodice, Deborah F. Pinney, Mark Heiges, Aaron J. Mackey, Jessica C. Kissinger, Haiming Wang, David S. Roos, John Brestelli, Xin Gao, Bindu Gajria, Jennifer Dommer, and Steve Fischer
Subjects: Genetics, Proteomics, Expressed sequence tag, Internet, biology, Protozoan Proteins, Toxoplasma gondii, Gene Expression, Genomics, Articles, biology.organism_classification, Genome, Genomic databases, Protozoan parasite, Systems Integration, Annotation, parasitic diseases, Databases, Genetic, Animals, Genome, Protozoan, Toxoplasma, Software
Abstract: ToxoDB (http://ToxoDB.org) is a genome and functional genomic database for the protozoan parasite Toxoplasma gondii. It incorporates the sequence and annotation of the T. gondii ME49 strain, as well as genome sequences for the GT1, VEG and RH (Chr Ia, Chr Ib) strains. Sequence information is integrated with various other genomic-scale data, including community annotation, ESTs, gene expression and proteomics data. ToxoDB has matured significantly since its initial release. Here we outline the numerous updates with respect to the data and increased functionality available on the website.
Published: 2007

23. Assessing performance of orthology detection strategies applied to eukaryotic genomes

Author: David S. Roos, Feng Chen, Jeroen K. Vermunt, Aaron J. Mackey, and Department of Methodology and Statistics
Subjects: Architecture domain, Science, Computational biology, Biology, Genome, Set (abstract data type), Consistency (database systems), Computational Biology/Protein Homology Detection, Genetics and Genomics/Genomics, Cluster analysis, Genetics, Multidisciplinary, Genetics and Genomics/Functional Genomics, Computational Biology, Genetics and Genomics, Inparanoid, Gold standard (test), Genetics and Genomics/Gene Function, Eukaryotic Cells, Medicine, Genetics and Genomics/Comparative Genomics, Functional genomics, Algorithms, Research Article, Computational Biology/Genomics
Abstract: Orthology detection is critically important for accurate functional annotation, and has been widely used to facilitate studies on comparative and evolutionary genomics. Although various methods are now available, there has been no comprehensive analysis of performance, due to the lack of a genomic-scale ‘gold standard’ orthology dataset. Even in the absence of such datasets, the comparison of results from alternative methodologies contains useful information, as agreement enhances confidence and disagreement indicates possible errors. Latent Class Analysis (LCA) is a statistical technique that can exploit this information to reasonably infer sensitivities and specificities, and is applied here to evaluate the performance of various orthology detection methods on a eukaryotic dataset. Overall, we observe a trade-off between sensitivity and specificity in orthology detection, with BLAST-based methods characterized by high sensitivity, and tree-based methods by high specificity. Two algorithms exhibit the best overall balance, with both sensitivity and specificity>80%: INPARANOID identifies orthologs across two species while OrthoMCL clusters orthologs from multiple species. Among methods that permit clustering of ortholog groups spanning multiple genomes, the (automated) OrthoMCL algorithm exhibits better within-group consistency with respect to protein function and domain architecture than the (manually curated) KOG database, and the homolog clustering algorithm TribeMCL as well. By way of using LCA, we are also able to comprehensively assess similarities and statistical dependence between various strategies, and evaluate the effects of parameter settings on performance. In summary, we present a comprehensive evaluation of orthology detection on a divergent set of eukaryotic genomes, thus providing insights and guides for method selection, tuning and development for different applications. Many biological questions have been addressed by multiple tests yielding binary (yes/no) outcomes but no clear definition of truth, making LCA an attractive approach for computational biology.
Published: 2007

24. Systems Analysis of Adaptive Responses to MAP Kinase Pathway Blockade in BRAF Mutant Melanoma

Author: Alexander F. Koeppel, Michael J. Weber, Devin G. Roller, Mark J. Axelrod, Aaron J. Mackey, Stefan Bekiranov, Daniel Gioeli, Brian J. Capaldo, Emanuel F. Petricoin, and Craig L. Slingluff
Subjects: Proto-Oncogene Proteins B-raf, MAPK/ERK pathway, Indoles, MAP Kinase Signaling System, Mutation, Missense, lcsh:Medicine, Drug resistance, Biology, Lapatinib, Receptor tyrosine kinase, Proto-Oncogene Protein c-ets-2, 03 medical and health sciences, 0302 clinical medicine, Downregulation and upregulation, ErbB, Cell Line, Tumor, medicine, Humans, lcsh:Science, skin and connective tissue diseases, Protein kinase A, Melanoma, 030304 developmental biology, Genetics, Sulfonamides, 0303 health sciences, Multidisciplinary, lcsh:R, 3. Good health, Amino Acid Substitution, Drug Resistance, Neoplasm, 030220 oncology & carcinogenesis, biology.protein, Cancer research, lcsh:Q, Research Article, medicine.drug
Abstract: Fifty percent of cutaneous melanomas are driven by activated BRAF V600E, but tumors treated with RAF inhibitors, even when they respond dramatically, rapidly adapt and develop resistance. Thus, there is a pressing need to identify the major mechanisms of intrinsic and adaptive resistance and develop drug combinations that target these resistance mechanisms. In a combinatorial drug screen on a panel of 12 treatment-naïve BRAF V600E mutant melanoma cell lines of varying levels of resistance to mitogen-activated protein kinase (MAPK) pathway inhibition, we identified the combination of PLX4720, a targeted inhibitor of mutated BRaf, and lapatinib, an inhibitor of the ErbB family of receptor tyrosine kinases, as synergistically cytotoxic in the subset of cell lines that displayed the most resistance to PLX4720. To identify potential mechanisms of resistance to PLX4720 treatment and synergy with lapatinib treatment, we performed a multi-platform functional genomics analysis to profile the genome as well as the transcriptional and proteomic responses of these cell lines to treatment with PLX4720. We found modest levels of resistance correlated with the zygosity of the BRAF V600E allele and receptor tyrosine kinase (RTK) mutational status. Layered over base-line resistance was substantial upregulation of many ErbB pathway genes in response to BRaf inhibition, thus generating the vulnerability to combination with lapatinib. The transcriptional responses of ErbB pathway genes are associated with a number of transcription factors, including ETS2 and its associated cofactors that represent a convergent regulatory mechanism conferring synergistic drug susceptibility in the context of diverse mutational landscapes.
Published: 2015
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25. The Genome of the Sea Urchin Strongylocentrotus purpuratus

Author: Amro Hamdoun, Virginia Brockton, Huyen Dinh, Qiang Tu, Richard O. Hynes, Maria Ina Arnone, Wratko Hlavina, L. Courtney Smith, Mariano A. Loza, David R. Burgess, Matthew P. Hoffman, Florian Raible, Qiu Autumn Yuan, Geoffrey Okwuonu, Mark Y. Tong, Jennifer Hume, Donna Maglott, Manisha Goel, Olivier Fedrigo, Manuel L. Gonzalez-Garay, Celina E. Juliano, Judith Hernandez, Gary M. Wessel, William F. Marzluff, Audrey J. Majeske, Christian Gache, Louise Duloquin, Xingzhi Song, François Lapraz, Fowler J, Alexandre Souvorov, Jared V. Goldstone, Georgia Panopoulou, Sandra Hines, Kyle M. Judkins, Clay Davis, Christine G. Elsik, Paul Kitts, Mariano Loza-Coll, Greg Wray, Taku Hibino, Eric Röttinger, Allison M. Churcher, Annamaria Locascio, Arcady Mushegian, Masashi Kinukawa, Anna Reade, Katherine M. Buckley, I. R. Gibbons, Bert Gold, Aleksandar Milosavljevic, David Epel, Victor D. Vacquier, Ling Ling Pu, Vincenzo Cavalieri, Erin L. Allgood, Lan Zhang, Lynne V. Nazareth, Constantin N. Flytzanis, Ian Bosdet, Yi-Hsien Su, Zeev Pancer, Matthew L. Rowe, Robert C. Angerer, David R. McClay, William H. Klein, Rachel F. Gray, Julian L. Wong, Shunsuke Yaguchi, Robert Bellé, Aaron J. Mackey, Herath Jayantha Gunaratne, Karl Frederik Bergeron, Bruce P. Brandhorst, Greg Murray, Avis H. Cohen, Stephanie Bell, Kristin Tessmar-Raible, Ian K. Townley, Bertrand Cosson, Thomas D. Glenn, Jongmin Nam, Cynthia A. Bradham, Michael Dean, Joseph Chacko, Anthony J. Robertson, Margherita Branno, Valeria Matranga, K. James Durbin, Esther Miranda, Lili Chen, Eran Elhaik, Robert D. Burke, Rita A. Wright, Paola Oliveri, Sandra L. Lee, Gary W. Moy, Alexander E Primus, Shawn S. McCafferty, Cristina Calestani, David A. Garfield, Erica Sodergren, Karen Wilson, Joel Smith, Marco A. Marra, Cynthia Messier, Julia Morales, Kim D. Pruitt, Rachel Thorn, Rachel Gill, John S. Taylor, Mark E. Hahn, Victor Sapojnikov, Meredith Howard-Ashby, Lynne M. Angerer, Maurice R. Elphick, Kathy R. Foltz, Anne Marie Genevière, Justin T. Reese, Blanca E. Galindo, Kim C. Worley, Andrew Leone, Glen Humphrey, Kevin Berney, Olga Ermolaeva, George Miner, David P. Terwilliger, Elly Suk Hen Chow, Lora Lewis, Dan Graur, C. Titus Brown, Gerard Manning, Kevin J. Peterson, Angela Jolivet, Michele K. Anderson, Francesca Rizzo, Ekaterina Voronina, Thierry Lepage, Giorgio Matassi, Antonio Fernandez-Guerra, Mamoru Nomura, Charles A. Whittaker, James R.R. Whittle, James A. Coffman, George M. Weinstock, Mohammed M. Idris, Ashlan M. Musante, Sebastian D. Fugmann, Katherine D. Walton, Sorin Istrail, Shu-Yu Wu, Cerrissa Hamilton, Jonah Cool, Jacqueline E. Schein, Stacey M. Curry, Athula Wikramanayke, Seth Carbonneau, Blair J. Rossetti, Christopher E. Killian, Melissa J. Landrum, Amanda P. Rawson, Jenifer C. Croce, Ryan C. Range, Rahul Satija, John J. Stegeman, Yufeng Shen, Cavit Agca, Terry Gaasterland, Rocky Cheung, Takae Kiyama, Nikki Adams, Jonathan P. Rast, Robert Piotr Olinski, Andrew Cree, Mark Scally, Shuguang Liang, David A. Parker, Rebecca Thomason, Gretchen E. Hofmann, Michelle M. Roux, Ronghui Xu, Robert A. Obar, Enrique Arboleda, Odile Mulner-Lorillon, Shannon Dugan-Rocha, David J. Bottjer, Gabriele Amore, Manoj P. Samanta, Waraporn Tongprasit, Véronique Duboc, La Ronda Jackson, Fred H. Wilt, Viktor Stolc, Anna T. Neill, Michael Raisch, Pei Yun Lee, Jia L. Song, Margaret Morgan, Brian T. Livingston, Sofia Hussain, Zheng Wei, Bryan J. Cole, Tonya F. Severson, Victor V. Solovyev, Finn Hallböök, Donna M. Muzny, Christine A. Byrum, Albert J. Poustka, Xiuqian Mu, Andrew R. Jackson, Shin Heesun, Euan R. Brown, Nansheng Chen, Patrick Cormier, Ralph Haygood, Pedro Martinez, R. Andrew Cameron, D. Wang, Wendy S. Beane, Eric H. Davidson, Christie Kovar, Hemant Kelkar, Charles A. Ettensohn, Sham V. Nair, Robert L. Morris, Stefan C. Materna, Michael C. Thorndyke, Richard A. Gibbs, Dan O Mellott, Department of Physiology and Biophysics, Stony Brook University [The State University of New York] ( SBU ), Astronomy Unit ( AU ), Queen Mary University of London ( QMUL ), Urban and Industrial Air Quality Group, CSIRO Energy Technology, Commonwealth Scientific and Industrial Research Organisation Energy Technology ( CSIRO Energy Technology ), Commonwealth Scientific and Industrial Research Organisation, Center for Polymer Studies ( CPS ), Boston University [Boston] ( BU ), Physics Department [Boston] ( BU-Physics ), Max Planck Institute for Psycholinguistics, Max-Planck-Institut, Department of Biology [Norton], Wheaton College [Norton], Mathematical Institute [Oxford] ( MI ), University of Oxford [Oxford], Centre for the Analysis of Time Series ( CATS ), London School of Economics and Political Science ( LSE ), Thomas Jefferson National Accelerator Facility ( Jefferson Lab ), Thomas Jefferson National Accelerator Facility, Laboratoire d'Energétique et de Mécanique Théorique Appliquée ( LEMTA ), Université de Lorraine ( UL ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire Evolution, Génomes et Spéciation ( LEGS ), Centre National de la Recherche Scientifique ( CNRS ), Department of Geology, University of Illinois at Urbana-Champaign [Urbana], Department of Electrical and Computer Engineering [Portland] ( ECE ), Portland State University [Portland] ( PSU ), Saint-Gobain Crystals [USA], SAINT-GOBAIN, Institute for Animal Health ( IAH ), Biotechnology and Biological Sciences Research Council, Center for Agricultural Resources Research, Chinese Academy of Sciences [Changchun Branch] ( CAS ), Ipsen Inc. [Milford] ( Ipsen ), IPSEN, Department of Physics [Berkeley], University of California [Berkeley], Institute for Climate and Atmospheric Science [Leeds] ( ICAS ), University of Leeds, Chung-Ang University ( CAU ), Chung-Ang University [Seoul], Antarctic Climate and Ecosystems Cooperative Research Center ( ACE-CRC ), Institute of Aerodynamics and Fluid Mechanics ( AER ), Technische Universität München [München] ( TUM ), Mer et santé ( MS ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Centre National de la Recherche Scientifique ( CNRS ), Imperial College London, Radio and Atmospheric Sciences Division, National Physical Laboratory [Teddington] ( NPL ), International Research Institute for Climate and Society ( IRI ), Earth Institute at Columbia University, Columbia University [New York]-Columbia University [New York], Soils Group, The Macaulay Institute, Department of Haematology, University of Cambridge [UK] ( CAM ), School of Biology and Biochemistry, Queen's University, Leslie Hill Institute for Plant Conservation ( PCU ), University of Cape Town, Institute for Microelectronics and Microsystems/ Istituto per la Microelettronica e Microsistemi ( IMM ), Consiglio Nazionale delle Ricerche ( CNR ), Laboratoire d'acoustique de l'université du Mans ( LAUM ), Le Mans Université ( UM ) -Centre National de la Recherche Scientifique ( CNRS ), Interactive Systems Labs ( ISL ), Carnegie Mellon University [Pittsburgh] ( CMU ), Dalian Institute of Chemical Physics ( DICP ), Architectures, Languages and Compilers to Harness the End of Moore Years ( ALCHEMY ), Laboratoire de Recherche en Informatique ( LRI ), Université Paris-Sud - Paris 11 ( UP11 ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -Université Paris-Sud - Paris 11 ( UP11 ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique ( Inria ), Clean Air Task Force ( CATF ), Clean Air Task Force, Space Physics Laboratory, Indian Space Research Organisation ( ISRO ), Centre d'études et de recherches appliquées à la gestion ( CERAG ), Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Centre National de la Recherche Scientifique ( CNRS ), Department of Microbiology and Immunology, College of Medicine and Health Sciences-Sultan Qaboos University, European Molecular Biology Laboratory [Heidelberg] ( EMBL ), Department of Biostatistics, University of Michigan [Ann Arbor], Department of Radiation Oncology [Michigan] ( Radonc ), Department of Physics and Astronomy [Leicester], University of Leicester, Informatique, Biologie Intégrative et Systèmes Complexes ( IBISC ), Université d'Évry-Val-d'Essonne ( UEVE ) -Centre National de la Recherche Scientifique ( CNRS ), Institut für Meteorologie und Klimaforschung ( IMK ), Karlsruher Institut für Technologie ( KIT ), Physics Department [UNB], University of New Brunswick ( UNB ), Laboratoire Parole et Langage ( LPL ), Centre National de la Recherche Scientifique ( CNRS ) -Aix Marseille Université ( AMU ), Institut des Sciences Chimiques de Rennes ( ISCR ), Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Ecole Nationale Supérieure de Chimie de Rennes-Institut National des Sciences Appliquées ( INSA ) -Centre National de la Recherche Scientifique ( CNRS ), Biogéosciences [Dijon] ( BGS ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS ), Bioprojet, Laboratoire de Matériaux à Porosité Contrôlée ( LMPC ), Université de Haute-Alsace (UHA) Mulhouse - Colmar ( Université de Haute-Alsace (UHA) ) -Ecole Nationale Supérieure de Chimie de Mulhouse-Centre National de la Recherche Scientifique ( CNRS ), School of Information Engineering [USTB] ( SIE ), University of Science and Technology Beijing [Beijing] ( USTB ), Laboratory for Atmospheric and Space Physics [Boulder] ( LASP ), University of Colorado Boulder [Boulder], Department of Applied Mathematics [Sheffield], University of Sheffield [Sheffield], School of Mathematics and Statistics [Sheffield] ( SoMaS ), Laboratoire de Mécanique de Lille - FRE 3723 ( LML ), Université de Lille, Sciences et Technologies-Ecole Centrale de Lille-Centre National de la Recherche Scientifique ( CNRS ), Computer Science Department [UCLA] ( CSD ), University of California at Los Angeles [Los Angeles] ( UCLA ), Développement et évolution ( DE ), Université Paris-Sud - Paris 11 ( UP11 ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire de Biologie du Développement de Villefranche sur mer ( LBDV ), Laboratoire Pierre Aigrain ( LPA ), Fédération de recherche du Département de physique de l'Ecole Normale Supérieure - ENS Paris ( FRDPENS ), Centre National de la Recherche Scientifique ( CNRS ) -École normale supérieure - Paris ( ENS Paris ) -Centre National de la Recherche Scientifique ( CNRS ) -École normale supérieure - Paris ( ENS Paris ) -Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Université Paris Diderot - Paris 7 ( UPD7 ) -Centre National de la Recherche Scientifique ( CNRS ), Department of Mathematics and Statistics [Mac Gill], McGill University, Departamento de Botánica [Comahue], Universidad nacional del Comahue, Bioénergétique Cellulaire et Pathologique ( BECP ), Université Joseph Fourier - Grenoble 1 ( UJF ) -Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ), Environnements et Paléoenvironnements OCéaniques ( EPOC ), Observatoire aquitain des sciences de l'univers ( OASU ), Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ) -École pratique des hautes études ( EPHE ) -Centre National de la Recherche Scientifique ( CNRS ), Institut Jacques Monod ( IJM ), Université Paris Diderot - Paris 7 ( UPD7 ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratori Nazionali del Sud ( LNS ), National Institute for Nuclear Physics ( INFN ), Departament de Matemàtiques [Barcelona], Universitat Autònoma de Barcelona [Barcelona] ( UAB ), Max-Planck-Institut für Kohlenforschung (coal research), Institute of Oceanology [CAS] ( IOCAS ), National Chiao Tung University ( NCTU ), Department of Hydrology and Water Resources ( HWR ), University of Arizona, Centre for Educational Technology, Environment Department [York], University of York [York, UK], State Key Laboratory of Nuclear Physics and Technology ( SKL-NPT ), Peking University [Beijing], Department of Physics and Astronomy [Iowa City], University of Iowa [Iowa], NASA Ames Research Center ( ARC ), Department of Materials, Digital Language & Knowledge Contents Research Association ( DICORA ), Hankuk University of Foreign Studies, Department of Physics [Coventry], University of Warwick [Coventry], Space Science and Technology Department [Didcot] ( RAL Space ), STFC Rutherford Appleton Laboratory ( RAL ), Science and Technology Facilities Council ( STFC ) -Science and Technology Facilities Council ( STFC ), Institut de biologie et chimie des protéines [Lyon] ( IBCP ), Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique ( CNRS ), H M Nautical Almanac Office [RAL] ( HMNAO ), Rutherford Appleton Laboratory, United Kingdom Met Office [Exeter], University College of London [London] ( UCL ), Department of Pathology and Laboratory Medicine [UCLA], University of California at Los Angeles [Los Angeles] ( UCLA ) -School of Medicine, School of Earth and Environmental Sciences [Seoul] ( SEES ), Seoul National University [Seoul], Department of Chemistry, Seoul Women's University, MicroMachines Centre ( MMC ), Nanyang Technological University [Singapour], Regroupement Québécois sur les Matériaux de Pointe ( RQMP ), École Polytechnique de Montréal ( EPM ) -Université de Sherbrooke [Sherbrooke]-McGill University-Université de Montréal-Fonds Québécois de Recherche sur la Nature et les Technologies ( FQRNT ), Département de Physique [Montréal], Université de Montréal, School of Earth and Environment [Leeds] ( SEE ), Centre for Ecology and Hydrology ( CEH ), Natural Environment Research Council ( NERC ), Norwegian Institute for Water Research ( NIVA ), Norwegian Institute for Water Research, Stony Brook University [SUNY] (SBU), State University of New York (SUNY)-State University of New York (SUNY), Astronomy Unit [London] (AU), Queen Mary University of London (QMUL), Commonwealth Scientific and Industrial Research Organisation Energy Technology (CSIRO Energy Technology), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Department of Biochemistry and Molecular Biology [Houston], The University of Texas Medical School at Houston, Mathematical Institute [Oxford] (MI), University of Oxford, Centre for the Analysis of Time Series (CATS), London School of Economics and Political Science (LSE), Thomas Jefferson National Accelerator Facility (Jefferson Lab), Laboratoire Énergies et Mécanique Théorique et Appliquée (LEMTA ), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Evolution, Génomes et Spéciation (LEGS), Centre National de la Recherche Scientifique (CNRS), University of Illinois System-University of Illinois System, Department of Electrical and Computer Engineering [Portland] (ECE), Portland State University [Portland] (PSU), Saint-Gobain, Institute for Animal Health (IAH), Biotechnology and Biological Sciences Research Council (BBSRC), Chinese Academy of Sciences [Changchun Branch] (CAS), Ipsen Inc. [Milford] (Ipsen), University of California [Berkeley] (UC Berkeley), University of California (UC)-University of California (UC), Institute for Climate and Atmospheric Science [Leeds] (ICAS), School of Earth and Environment [Leeds] (SEE), University of Leeds-University of Leeds, Chung-Ang University (CAU), Antarctic Climate and Ecosystems Cooperative Research Centre (ACE-CRC), Institute of Aerodynamics and Fluid Mechanics (AER), Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), Mer et santé (MS), Station biologique de Roscoff [Roscoff] (SBR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), National Physical Laboratory [Teddington] (NPL), International Research Institute for Climate and Society (IRI), Macaulay Institute, University of Cambridge [UK] (CAM), Queen's University [Kingston, Canada], Leslie Hill Institute for Plant Conservation (PCU), Istituto per la Microelettronica e Microsistemi [Catania] (IMM), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), Laboratoire d'Acoustique de l'Université du Mans (LAUM), Le Mans Université (UM)-Centre National de la Recherche Scientifique (CNRS), Interactive Systems Labs (ISL), Carnegie Mellon University [Pittsburgh] (CMU), Dalian Institute of Chemical Physics (DICP), Architectures, Languages and Compilers to Harness the End of Moore Years (ALCHEMY), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Clean Air Task Force (CATF), Indian Space Research Organisation (ISRO), Centre d'études et de recherches appliquées à la gestion (CERAG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Centre National de la Recherche Scientifique (CNRS), Sultan Qaboos University (SQU)-College of Medicine and Health Sciences [Baylor], Baylor University-Baylor University, European Molecular Biology Laboratory [Heidelberg] (EMBL), University of Michigan System-University of Michigan System, Department of Radiation Oncology [Michigan] (Radonc), Informatique, Biologie Intégrative et Systèmes Complexes (IBISC), Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), Institute for Meteorology and Climate Research (IMK), Karlsruhe Institute of Technology (KIT), University of New Brunswick (UNB), Laboratoire Parole et Langage (LPL), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Institut des Sciences Chimiques de Rennes (ISCR), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Ecole Nationale Supérieure de Chimie de Rennes (ENSCR)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Biogéosciences [UMR 6282] (BGS), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Matériaux à Porosité Contrôlée (LMPC), Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS), School of Information Engineering [USTB] (SIE), University of Science and Technology Beijing [Beijing] (USTB), Laboratory for Atmospheric and Space Physics [Boulder] (LASP), University of Colorado [Boulder], School of Mathematics and Statistics [Sheffield] (SoMaS), Laboratoire de Mécanique de Lille - FRE 3723 (LML), Université de Lille, Sciences et Technologies-Centrale Lille-Centre National de la Recherche Scientifique (CNRS), Computer Science Department [UCLA] (CSD), University of California [Los Angeles] (UCLA), Développement et évolution (DE), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Biologie du Développement de Villefranche sur mer (LBDV), Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Pierre Aigrain (LPA), Fédération de recherche du Département de physique de l'Ecole Normale Supérieure - ENS Paris (FRDPENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Department of Mathematics and Statistics [Montréal], McGill University = Université McGill [Montréal, Canada], Departamento de Botánica [Bariloche], Centro Regional Universitario Bariloche [Bariloche] (CRUB), Universidad Nacional del Comahue [Neuquén] (UNCOMA)-Universidad Nacional del Comahue [Neuquén] (UNCOMA), Bioénergétique Cellulaire et Pathologique (BECP), Université Joseph Fourier - Grenoble 1 (UJF)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Environnements et Paléoenvironnements OCéaniques (EPOC), Observatoire aquitain des sciences de l'univers (OASU), Université Sciences et Technologies - Bordeaux 1 (UB)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Sciences et Technologies - Bordeaux 1 (UB)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Institut Jacques Monod (IJM (UMR_7592)), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Laboratori Nazionali del Sud (LNS), Istituto Nazionale di Fisica Nucleare (INFN), Departament de Matemàtiques [Barcelona] (UAB), Universitat Autònoma de Barcelona (UAB), Max-Planck-Institut für Kohlenforschung (Coal Research), Max-Planck-Gesellschaft, CAS Institute of Oceanology (IOCAS), Chinese Academy of Sciences [Beijing] (CAS), National Chiao Tung University (NCTU), Department of Hydrology and Water Resources (HWR), State Key Laboratory of Nuclear Physics and Technology (SKL-NPT), University of Iowa [Iowa City], NASA Ames Research Center (ARC), Digital Language & Knowledge Contents Research Association (DICORA), Space Science and Technology Department [Didcot] (RAL Space), STFC Rutherford Appleton Laboratory (RAL), Science and Technology Facilities Council (STFC)-Science and Technology Facilities Council (STFC), Institut de biologie et chimie des protéines [Lyon] (IBCP), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), H M Nautical Almanac Office [RAL] (HMNAO), University College of London [London] (UCL), University of California (UC)-University of California (UC)-School of Medicine, School of Earth and Environmental Sciences [Seoul] (SEES), Seoul National University [Seoul] (SNU), MicroMachines Centre (MMC), Regroupement Québécois sur les Matériaux de Pointe (RQMP), École Polytechnique de Montréal (EPM)-Université de Sherbrooke (UdeS)-McGill University = Université McGill [Montréal, Canada]-Université de Montréal (UdeM)-Fonds Québécois de Recherche sur la Nature et les Technologies (FQRNT), Université de Montréal (UdeM), Centre for Ecology and Hydrology (CEH), Natural Environment Research Council (NERC), Norwegian Institute for Water Research (NIVA), SEA URCHIN GENOME SEQUENCING CONSORTIUM, SODERGREN E, WEINSTOCK GM, DAVIDSON EH, CAMERON RA, GIBBS RA, ANGERER RC, ANGERER LM, ARNONE MI, BURGESS DR, BURKE RD, COFFMAN JA, DEAN M, ELPHICK MR, ETTENSOHN CA, FOLTZ KR, HAMDOUN A, HYNES RO, KLEIN WH, MARZLUFF W, MCCLAY DR, MORRIS RL, MUSHEGIAN A, RAST JP, SMITH LC, THORNDYKE MC, VACQUIER VD, WESSEL GM, WRAY G, ZHANG L, ELSIK CG, ERMOLAEVA O, HLAVINA W, HOFMANN G, KITTS P, LANDRUM MJ, MACKEY AJ, MAGLOTT D, PANOPOULOU G, POUSTKA AJ, PRUITT K, SAPOJNIKOV V, SONG X, SOUVOROV A, SOLOVYEV V, WEI Z, WHITTAKER CA, WORLEY K, DURBIN KJ, SHEN Y, FEDRIGO O, GARFIELD D, HAYGOOD R, PRIMUS A, SATIJA R, SEVERSON T, GONZALEZ-GARAY ML, JACKSON AR, MILOSAVLJEVIC A, TONG M, KILLIAN CE, LIVINGSTON BT, WILT FH, ADAMS N, BELLE R, CARBONNEAU S, CHEUNG R, CORMIER P, COSSON B, CROCE J, FERNANDEZ-GUERRA A, GENEVIERE AM, GOEL M, KELKAR H, MORALES J, MULNER-LORILLON O, ROBERTSON AJ, GOLDSTONE JV, COLE B, EPEL D, GOLD B, HAHN ME, HOWARD-ASHBY M, SCALLY M, STEGEMAN JJ, ALLGOOD EL, COOL J, JUDKINS KM, MCCAFFERTY SS, MUSANTE AM, OBAR RA, RAWSON AP, ROSSETTI BJ, GIBBONS IR, HOFFMAN MP, LEONE A, ISTRAIL S, MATERNA SC, SAMANTA MP, STOLC V, TONGPRASIT W, TU Q, BERGERON KF, BRANDHORST BP, WHITTLE J, BERNEY K, BOTTJER DJ, CALESTANI C, PETERSON K, CHOW E, YUAN QA, ELHAIK E, GRAUR D, REESE JT, BOSDET I, HEESUN S, MARRA MA, SCHEIN J, ANDERSON MK, BROCKTON V, BUCKLEY KM, COHEN AH, FUGMANN SD, HIBINO T, LOZA-COLL M, MAJESKE AJ, MESSIER C, NAIR SV, PANCER Z, TERWILLIGER DP, AGCA C, ARBOLEDA E, CHEN N, CHURCHER AM, HALLBOOK F, HUMPHREY GW, IDRIS MM, KIYAMA T, LIANG S, MELLOTT D, MU X, MURRAY G, OLINSKI RP, RAIBLE F, ROWE M, TAYLOR JS, TESSMAR-RAIBLE K, WANG D, WILSON KH, YAGUCHI S, GAASTERLAND T, GALINDO BE, GUNARATNE HJ, JULIANO C, KINUKAWA M, MOY GW, NEILL AT, NOMURA M, RAISCH M, READE A, ROUX MM, SONG JL, SU YH, TOWNLEY IK, VORONINA E, WONG JL, AMORE G, BRANNO M, BROWN ER, CAVALIERI, V, DUBOC V, DULOQUIN L, FLYTZANIS C, GACHE C, LAPRAZ F, LEPAGE T, LOCASCIO A, MART, University of California-University of California, Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC), Consiglio Nazionale delle Ricerche (CNR), Centre National de la Recherche Scientifique (CNRS)-Le Mans Université (UM), Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Ecole Nationale Supérieure de Chimie de Rennes (ENSCR)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Biogéosciences [UMR 6282] [Dijon] (BGS), Centre National de la Recherche Scientifique (CNRS)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Ecole Nationale Supérieure de Chimie de Mulhouse-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Fédération de recherche du Département de physique de l'Ecole Normale Supérieure - ENS Paris (FRDPENS), Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-École pratique des hautes études (EPHE), University of California-University of California-School of Medicine, Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Ecole Nationale Supérieure de Chimie de Rennes (ENSCR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Université de Lille, Sciences et Technologies-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille, Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Joseph Fourier - Grenoble 1 (UJF), University of Manchester Institute of Science and Technology (UMIST), Condensed Matter Physics and Materials Science Department, Brookhaven National Laboratory, Brookhaven National Laboratory [Upton, NY] (BNL), UT-Battelle, LLC-Stony Brook University [SUNY] (SBU), State University of New York (SUNY)-State University of New York (SUNY)-U.S. Department of Energy [Washington] (DOE)-UT-Battelle, LLC-Stony Brook University [SUNY] (SBU), State University of New York (SUNY)-State University of New York (SUNY)-U.S. Department of Energy [Washington] (DOE), Baylor College of Medicine (BCM), Baylor University, Laboratoire de Traitement de l'Information Medicale (LaTIM), Université européenne de Bretagne - European University of Brittany (UEB)-Université de Brest (UBO)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest), Laboratoire de Modélisation et Simulation Multi Echelle (MSME), Université Paris-Est Marne-la-Vallée (UPEM)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS), Duke University [Durham], Instituto Andaluz de Geofísica y Prevención de Desastres Sísmicos [Granada] (IAGPDS), Universidad de Granada (UGR), Laboratoire d'Ingénierie des Matériaux de Bretagne (LIMATB), Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université de Brest (UBO), University of New South Wales [Sydney] (UNSW), Celera Genomics (CRA), Celera Genomics, Paléobiodiversité et paléoenvironnements, Muséum national d'Histoire naturelle (MNHN)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Università degli Studi di Roma Tor Vergata [Roma], Unité de recherches forestières (BORDX PIERR UR ), Institut National de la Recherche Agronomique (INRA), Deptartment of Neuroscience, Uppsala University, State Key Laboratory of Palaeobiology and Stratigraphy, Nanjing Institute of Geology and Palaeontology (NIGPAS-CAS), Chinese Academy of Sciences [Nanjing Branch]-Chinese Academy of Sciences [Nanjing Branch], Institut Méditerranéen d'Ecologie et de Paléoécologie (IMEP), Université Paul Cézanne - Aix-Marseille 3-Université de Provence - Aix-Marseille 1-Avignon Université (AU)-Centre National de la Recherche Scientifique (CNRS), Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China, Université Paris Diderot - Paris 7 (UPD7), Department of Physical and Environmental Sciences [Toronto], University of Toronto at Scarborough, inconnu temporaire UPEMLV, Inconnu, Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Department of Atmospheric Sciences [Seattle], University of Washington [Seattle], National Institute of Advanced Industrial Science and Technology (AIST), Department of Pharmacy, Università degli studi di Genova = University of Genoa (UniGe), Interdisciplinary Arts and Sciences Department, St. Vincent's Hospital, Sydney, Laboratoire des Sciences de l'Environnement Marin (LEMAR) (LEMAR), Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Institut Universitaire Européen de la Mer (IUEM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Department of Electrical Engineering (DEE-POSTECH), Pohang University of Science and Technology (POSTECH), Centre Suisse d'Electronique et de Microtechnique SA [Neuchatel] (CSEM), Centre Suisse d'Electronique et Microtechnique SA (CSEM), Human Genome Sequencing Center [Houston] (HGSC), Brookhaven National Laboratory, Meteorological Service of Canada, 4905 Dufferin Street, Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Mines-Télécom [Paris] (IMT), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Université Paris-Est Marne-la-Vallée (UPEM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Unité de Recherches Forestières, Department of Physical and Environmental Sciences, University of Toronto [Scarborough, Canada], National Institute for Nuclear Physics (INFN), University of Genoa (UNIGE), Institut de Recherche pour le Développement (IRD)-Institut Universitaire Européen de la Mer (IUEM), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université de Brest (UBO)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS), Universidad de Granada = University of Granada (UGR), Laboratoire d'Energétique et de Mécanique Théorique Appliquée (LEMTA ), Technische Universität München [München] (TUM), Queen's University [Kingston], Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Grenoble Alpes (UGA), Institut für Meteorologie und Klimaforschung (IMK), Karlsruher Institut für Technologie (KIT), Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Supérieure de Chimie de Rennes-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES), Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Ecole Centrale de Lille-Université de Lille, Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-École pratique des hautes études (EPHE)-Centre National de la Recherche Scientifique (CNRS), Universitat Autònoma de Barcelona [Barcelona] (UAB), École Polytechnique de Montréal (EPM)-Université de Sherbrooke [Sherbrooke]-Université de Montréal [Montréal]-McGill University-Fonds Québécois de Recherche sur la Nature et les Technologies (FQRNT), Université de Montréal [Montréal], U.S. Department of Energy [Washington] (DOE)-UT-Battelle, LLC-Stony Brook University [SUNY] (SBU), Université de Bretagne Sud (UBS)-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université de Brest (UBO)-Université de Brest (UBO), Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC), Université Paul Cézanne - Aix-Marseille 3-Centre National de la Recherche Scientifique (CNRS)-Avignon Université (AU)-Université de Provence - Aix-Marseille 1, Institut Universitaire Européen de la Mer (IUEM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Centre National de la Recherche Scientifique (CNRS)-Université de Brest (UBO), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Université de Lille, Sciences et Technologies-Ecole Centrale de Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), École normale supérieure - Paris (ENS Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Male, MESH: Signal Transduction, MESH: Sequence Analysis, DNA, MESH : Transcription Factors, MESH : Calcification, Physiologic, Genome, MESH : Proteins, 0302 clinical medicine, MESH : Embryonic Development, MESH: Gene Expression Regulation, Developmental, Innate, MESH: Embryonic Development, Developmental, Nervous System Physiological Phenomena, MESH: Animals, MESH: Proteins, [SDV.BDD]Life Sciences [q-bio]/Development Biology, Complement Activation, ComputingMilieux_MISCELLANEOUS, MESH: Evolution, Molecular, MESH : Strongylocentrotus purpuratus, Genetics, 0303 health sciences, MESH: Nervous System Physiological Phenomena, Multidisciplinary, biology, Medicine (all), MESH: Immunologic Factors, Gene Expression Regulation, Developmental, Genome project, MESH: Transcription Factors, MESH : Immunity, Innate, MESH : Complement Activation, MESH: Genes, Bacterial artificial chromosome (BAC)DeuterostomesStrongylocentrotus purpuratusVertebrate innovations, Echinoderm, MESH : Nervous System Physiological Phenomena, embryonic structures, MESH: Cell Adhesion Molecules, MESH : Genes, MESH: Immunity, Innate, Sequence Analysis, Signal Transduction, MESH: Computational Biology, Genome evolution, MESH: Complement Activation, Sequence analysis, Evolution, MESH: Strongylocentrotus purpuratus, MESH : Male, Embryonic Development, MESH : Immunologic Factors, Article, MESH: Calcification, Physiologic, Calcification, MESH : Cell Adhesion Molecules, Evolution, Molecular, 03 medical and health sciences, Calcification, Physiologic, Animals, Immunologic Factors, MESH: Genome, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, MESH : Evolution, Molecular, Physiologic, Gene, Strongylocentrotus purpuratus, [ SDV.BBM ] Life Sciences [q-bio]/Biochemistry, Molecular Biology, 030304 developmental biology, MESH : Signal Transduction, Bacterial artificial chromosome, Immunity, Molecular, Computational Biology, Proteins, Cell Adhesion Molecules, Genes, Immunity, Innate, Transcription Factors, Sequence Analysis, DNA, DNA, biology.organism_classification, MESH: Male, Gene Expression Regulation, MESH : Animals, MESH : Gene Expression Regulation, Developmental, MESH : Genome, 030217 neurology & neurosurgery, MESH : Computational Biology, MESH : Sequence Analysis, DNA
Abstract: We report the sequence and analysis of the 814-megabase genome of the sea urchin Strongylocentrotus purpuratus , a model for developmental and systems biology. The sequencing strategy combined whole-genome shotgun and bacterial artificial chromosome (BAC) sequences. This use of BAC clones, aided by a pooling strategy, overcame difficulties associated with high heterozygosity of the genome. The genome encodes about 23,300 genes, including many previously thought to be vertebrate innovations or known only outside the deuterostomes. This echinoderm genome provides an evolutionary outgroup for the chordates and yields insights into the evolution of deuterostomes.
Published: 2006
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26. Insights into social insects from the genome of the honeybee Apis mellifera

Author: George M. Weinstock, Andrew K. Jones, Katherine A Aronstein, Irene Gattermeier, Kiyoshi Kimura, Susan E. Fahrbach, Laura I. Decanini, Christina M. Grozinger, Evgeny M. Zdobnov, Susan J. Brown, Jonathan V. Sweedler, Kazutoyo Osoegawa, Christian A. Ross, Joseph J. Gillespie, Ngoc Nguyen, Geert Baggerman, Frank Hauser, Dan Graur, Michelle M. Elekonich, Alison R. Mercer, Amanda F. Svatek, Jean Marie Cornuet, Cornelis J. P. Grimmelikhuijzen, Aleksandar Milosavljevic, Anand Venkatraman, Andrew J. Schroeder, Huaiyang Jiang, Michael R. Kanost, Justin T. Reese, Margaret Morgan, Tomoko Fujiyuki, Kim C. Worley, Susanta K. Behura, Jun Kawai, Robert Kucharski, Gildardo Aquino-Perez, Miguel Corona, Diana E. Wheeler, Kathryn S. Campbell, William M. Gelbart, Amy L. Toth, Yanping Chen, Mira Cohen, Noam Kaplan, Michihira Tagami, Miguel A. Peinado, Peter K. Dearden, Glenford Savery, Liliane Schoofs, Takeo Kubo, Giuseppe Cazzamali, Sylvain Forêt, Thomas C. Newman, Ross Overbeek, Piero Carninci, Ryszard Maleszka, Barbara J. Ruef, Michal Linial, Alexandre S. Cristino, Mary A. Schuler, Huyen Dinh, J. Troy Littleton, Manoj P. Samanta, Waraporn Tongprasit, L. Sian Grametes, Eran Elhaik, Jean-Luc Imler, Zhen Zou, Rodrigo A. Velarde, Tanja Gempe, Dorothea Eisenhardt, Juan Manuel Anzola, Graham J. Thompson, Aaron J. Mackey, René Feyereisen, Mrcia M.G. Bitondi, Lora Lewis, Guy Bloch, Richard A. Gibbs, Jane Peterson, Jay D. Evans, Robert E. Page, Amanda B. Hummon, Viktor Stolc, Donna M. Muzny, Yair Shemesh, Francis M. F. Nunes, Dawn Lopez, Judith H. Willis, Martin Hasselmann, Mark S. Guyer, John G. Oakeshott, Pinglei Zhou, Eriko Kage, Dominique Vautrin, Kevin J. Hackett, Sandra L. Lee, Clay Davis, Christine Emore, Gene E. Robinson, Alexandre Souvorov, T.A. Richmond, Rachel Thorn, Jurgen Huybrechts, Elad B. Rubin, Craig Mizzen, Deborah R. Smith, Walter S. Sheppard, Takekazu Kunieda, Adam Felsenfeld, Bingshan Li, Jeffrey G. Reid, La Ronda Jackson, Jamie J. Cannone, Robin R. Gutell, Jireh Santibanez, Megan J. Wilson, David B. Sattelle, Azusa Kamikouchi, George Miner, Hideaki Takeuchi, Geoffrey Okwuonu, Jennifer Hume, Jonathan Miller, Kazuaki Ohashi, Angela Jovilet, Yoshihide Hayashizaki, Joseph Chacko, Paul Kitts, Erica Sodergren, Charles Hetru, Andrew V. Suarez, Brian P. Lazzaro, Susan E. St. Pierre, Evy Vierstraete, Haobo Jiang, Sandra Hines, Teresa D. Shippy, Greg J. Hunt, Peter Kosarev, Dan Hultmark, Stefan Albert, Susan M. Russo, Chung Li Shu, Michel Solignac, H. Michael G. Lattorff, Xu Ling, Grard Leboulle, Miklós Csürös, Neil D. Tsutsui, Lynne V. Nazareth, Ying Wang, Florence Mougel, Beverly B. Matthews, Kevin L. Childs, Rita A. Wright, Hugh M. Robertson, Lan Zhang, Peter Verleyen, Daniel B. Weaver, Christie Kovar, Chikatoshi Kai, Charles W. Whitfield, Madeline A. Crosby, Natalia V. Milshina, Reed M. Johnson, Michael A. Ewing, Peter L. Jones, Sandra L. Rodriguez-Zas, Michael B. Eisen, Klaus Hartfelder, Karl H.J. Gordon, W. Augustine Dunn, Ling Ling Pu, M. Monnerot, Stephen Richards, Richa Agarwala, Judith Hernandez, Pieter J. de Jong, Michael Williamson, Marcé D. Lorenzen, Zilá Luz Paulino Simões, Mark D. Drapeau, Donna Villasana, Katarína Bíliková, J. Spencer Johnston, David I. Schlipalius, Xuehong Wei, Laurent Duret, Venky N. Iyer, Andrew G. Clark, Christine G. Elsik, Hilary Ranson, Kyle T. Beggs, Mireia Jordà, Shiro Fukuda, Seth A. Ament, Vivek Iyer, Jozef Šimúth, Stewart H. Berlocher, May R. Berenbaum, Robin F. A. Moritz, Tatsuhiko Kadowaki, Charles Claudianos, Gro V. Amdam, Yue Liu, Naoko Sakazume, Morten Schioett, Paul Havlak, Anita M. Collins, Dirk C. de Graaf, Derek Collinge, Ivica Letunic, Carlos H. Lobo, Mizue Morioka, Martin Beye, Rachel Gill, C. Michael Dickens, Daisuke Sasaki, Victor V. Solovyev, Peer Bork, Sunita Biswas, David A. Wheeler, Heidi Paul, Bioinformatique, phylogénie et génomique évolutive (BPGE), Département PEGASE [LBBE] (PEGASE), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Evolution, Génomes et Spéciation (LEGS), Centre National de la Recherche Scientifique (CNRS), and Physical and genetic mapping
Subjects: Male, 0106 biological sciences, Transposable element, [SDV.OT]Life Sciences [q-bio]/Other [q-bio.OT], Proteome, Genome, Insect, Molecular Sequence Data, Genes, Insect, Genomics, Biology, 010603 evolutionary biology, 01 natural sciences, Genome, Article, Evolution, Molecular, 03 medical and health sciences, Molecular evolution, Phylogenetics, Animals, Gene, Phylogeny, abeille domestique, 030304 developmental biology, Whole genome sequencing, Genetics, Base Composition, 0303 health sciences, [SDV.GEN]Life Sciences [q-bio]/Genetics, Multidisciplinary, Behavior, Animal, Reproduction, SOCIAL BEHAVIOR, [SDV.BA]Life Sciences [q-bio]/Animal biology, Immunity, APIS MELLIFERA, food and beverages, Bees, Telomere, Physical Chromosome Mapping, INSECTE, Gene Expression Regulation, DNA methylation, DNA Transposable Elements, Female, GENETIQUE DES POPULATIONS, Signal Transduction
Abstract: Ce travail résulte de la collaboration de très nombreux chercheurs. Seuls les auteurs de la rubrique Physical and Genetic Mapping sont cités explicitement.; Here we report the genome sequence of the honeybee Apis mellifera, a key model for social behaviour and essential to global ecology through pollination. Compared with other sequenced insect genomes, the A. mellifera genome has high A1T and CpG contents, lacks major transposon families, evolves more slowly, and is more similar to vertebrates for circadian rhythm, RNA interference and DNA methylation genes, among others. Furthermore, A.mellifera has fewer genes for innate immunity, detoxification enzymes, cuticle-forming proteins and gustatory receptors, more genes for odorant receptors, and novel genes for nectar and pollen utilization, consistent with its ecology and social organization. Compared to Drosophila, genes in early developmental pathways differ in Apis, whereas similarities exist for functions that differ markedly, such as sex determination, brain function and behaviour. Population genetics suggests a novel African origin for the species A.mellifera and insights into whether Africanized bees spread throughout the New World via hybridization or displacement
Published: 2006
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27. The transcriptome of Toxoplasma gondii

Author: Michael S. Behnke, Jay R. Radke, David S. Roos, Joshua B. Radke, Aaron J. Mackey, and Michael W. White
Subjects: Transcription, Genetic, Physiology, Genes, Protozoan, 030231 tropical medicine, Plant Science, Polymerase Chain Reaction, Polymorphism, Single Nucleotide, General Biochemistry, Genetics and Molecular Biology, Transcriptome, 03 medical and health sciences, 0302 clinical medicine, Structural Biology, Gene expression, Animals, RNA, Messenger, Serial analysis of gene expression, lcsh:QH301-705.5, Gene, Ecology, Evolution, Behavior and Systematics, DNA Primers, 030304 developmental biology, Expressed Sequence Tags, Regulation of gene expression, Genetics, 0303 health sciences, Expressed sequence tag, Base Sequence, biology, Toxoplasma gondii, Cell Biology, biology.organism_classification, Phenotype, lcsh:Biology (General), Gene Expression Regulation, Poly A, General Agricultural and Biological Sciences, Toxoplasma, RNA, Protozoan, Research Article, Developmental Biology, Biotechnology
Abstract: BackgroundToxoplasma gondiigives rise to toxoplasmosis, among the most prevalent parasitic diseases of animals and man. Transformation of the tachzyoite stage into the latent bradyzoite-cyst form underlies chronic disease and leads to a lifetime risk of recrudescence in individuals whose immune system becomes compromised. Given the importance of tissue cyst formation, there has been intensive focus on the development of methods to study bradyzoite differentiation, although the molecular basis for the developmental switch is still largely unknown.ResultsWe have used serial analysis of gene expression (SAGE) to define theToxoplasma gondiitranscriptome of the intermediate-host life cycle that leads to the formation of the bradyzoite/tissue cyst. A broad view of gene expression is provided by >4-fold coverage from nine distinct libraries (~300,000 SAGE tags) representing key developmental transitions in primary parasite populations and in laboratory strains representing the three canonical genotypes. SAGE tags, and their corresponding mRNAs, were analyzed with respect to abundance, uniqueness, and antisense/sense polarity and chromosome distribution and developmental specificity.ConclusionThis study demonstrates that phenotypic transitions during parasite development were marked by unique stage-specific mRNAs that accounted for 18% of the total SAGE tags and varied from 1–5% of the tags in each developmental stage. We have also found thatToxoplasmamRNA pools have a unique parasite-specific composition with 1 in 5 transcripts encoding Apicomplexa-specific genes functioning in parasite invasion and transmission. Developmentally co-regulated genes were dispersed across allToxoplasmachromosomes, as were tags representing each abundance class, and a variety of biochemical pathways indicating that trans-acting mechanisms likely control gene expression in this parasite. We observed distinct similarities in the specificity and expression levels of mRNAs in primary populations (Day-6 post-sporozoite infection) that occur prior to the onset of bradyzoite development that were uniquely shared with the virulent Type I-RH laboratory strain suggesting that development of RH may be arrested. By contrast, strains from Type II-Me49B7 and Type III-VEGmsj contain SAGE tags corresponding to bradyzoite genes, which suggests that priming of developmental expression likely plays a role in the greater capacity of these strains to complete bradyzoite development.
Published: 2005
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28. Composite genome map and recombination parameters derived from three archetypal lineages of Toxoplasma gondii

Author: Asis Khan, Robert H. Cole, Keliang Tang, Sonya Taylor, David S. Roos, Chunlei Su, John C. Wootton, Darius Glover, John C. Boothroyd, Jitender P. Dubey, L. David Sibley, Aaron J. Mackey, Jon P. Boyle, E. R. Pfefferkorn, Ian T. Paulsen, Matthew Berriman, and James W. Ajioka
Subjects: Genetic Linkage, Outbreeding depression, Population, Molecular Sequence Data, Drug Resistance, Biology, Genetic analysis, Genome, Polymorphism, Single Nucleotide, Chromosomes, Article, 03 medical and health sciences, Genetic linkage, parasitic diseases, Genetics, Animals, education, Gene, Crosses, Genetic, 030304 developmental biology, Whole genome sequencing, Expressed Sequence Tags, Recombination, Genetic, 0303 health sciences, Expressed sequence tag, education.field_of_study, Base Sequence, 030306 microbiology, Chromosome Mapping, 3. Good health, Databases, Nucleic Acid, Genome, Protozoan, Toxoplasma, Polymorphism, Restriction Fragment Length
Abstract: Toxoplasma gondii is a highly successful protozoan parasite in the phylum Apicomplexa, which contains numerous animal and human pathogens. T.gondii is amenable to cellular, biochemical, molecular and genetic studies, making it a model for the biology of this important group of parasites. To facilitate forward genetic analysis, we have developed a high-resolution genetic linkage map for T.gondii. The genetic map was used to assemble the scaffolds from a 10X shotgun whole genome sequence, thus defining 14 chromosomes with markers spaced at approximately 300 kb intervals across the genome. Fourteen chromosomes were identified comprising a total genetic size of approximately 592 cM and an average map unit of approximately 104 kb/cM. Analysis of the genetic parameters in T.gondii revealed a high frequency of closely adjacent, apparent double crossover events that may represent gene conversions. In addition, we detected large regions of genetic homogeneity among the archetypal clonal lineages, reflecting the relatively few genetic outbreeding events that have occurred since their recent origin. Despite these unusual features, linkage analysis proved to be effective in mapping the loci determining several drug resistances. The resulting genome map provides a framework for analysis of complex traits such as virulence and transmission, and for comparative population genetic studies.
Published: 2005

29. A strategy for the rapid identification of phosphorylation sites in the phosphoproteome

Author: William R. Pearson, Timothy A.J. Haystead, Aaron J. Mackey, and Justin A. MacDonald
Subjects: Sequence analysis, Proteolysis, In silico, Molecular Sequence Data, Biology, Cleavage (embryo), Biochemistry, Sensitivity and Specificity, Analytical Chemistry, chemistry.chemical_compound, medicine, Amino Acid Sequence, Phosphorylation, Molecular Biology, Peptide sequence, Internet, medicine.diagnostic_test, Edman degradation, Proteins, Sequence Analysis, DNA, Molecular biology, Recombinant Proteins, Neoplasm Proteins, chemistry, Databases as Topic, Neural Networks, Computer, DNA, Software
Abstract: Edman phosphate ((32)P) release sequencing provides a high sensitivity means of identifying phosphorylation sites in proteins that complements mass spectrometry techniques. We have developed a bioinformatic assessment tool, the cleavage of radiolabeled protein (CRP) program, which enables experimental identification of phosphorylation sites via (32)P labeling and Edman degradation of cleaved proteins obtained at femtomole levels. By observing the Edman cycle(s) in which radioactivity is found, candidate phosphorylation sites are identified by determining which residues occur at the observed number of cycles downstream from a peptide cleavage site. In cases where more than one residue could be responsible for the observed radioactivity, additional experiments with cleavage reagents having alternative specificities may resolve the ambiguity. Given a protein sequence and a cleavage site, CRP performs these experiments in silico, identifying resolved sites based on user-supplied experimental data, as well as suggesting combinations of reagents for additional analyses. Analysis of the PhosphoBase protein sequence database suggests that CRP data from two cleavage experiments can be used to identify unambiguously 60% of known phosphorylation sites. Data from additional cleavage experiments may increase the overall coverage to 70% of known sites. By comparing theoretical data obtained from the CRP program with (32)P release data obtained from an Edman sequencer, a known phosphorylation site was identified unambiguously and correctly. In addition, our results show that in vivo phosphorylation sites can be determined routinely by differential proteolysis analysis and Edman cycling with less than 1 fmol of protein and 1000 cpm.
Published: 2002

30. Abstract 4166: Integrated molecular profiling of melanoma cell lines reveals genotype-drug phenotype associations giving insight on development of combination therapies

Author: Stefan Bekiranov, Michael J. Weber, Aaron J. Mackey, Dan Gioeli, Devin G. Roller, Brian J. Capaldo, Mark J. Axelrod, and Alexander F. Koeppel
Subjects: Genetics, Cancer Research, medicine.medical_treatment, Drug resistance, Cell cycle, Biology, Lapatinib, Targeted therapy, Oncology, DNA methylation, Cancer research, medicine, Epigenetics, DNA microarray, V600E, medicine.drug
Abstract: Integrated molecular profiling of melanoma cell lines reveals genotype-drug phenotype associations giving insight on development of combination therapies Adaptive survival responses to targeted therapy reduce therapeutic benefit, and thus there is a need to identify drug combinations that co-target both oncogenic drivers and critical adaptive responses. We screened a panel of twelve V600E BRAF melanoma cell lines with a library of targeted drugs, and identified the combination of lapatinib (a HER family inhibitor) plus PLX4720 (a RAF inhibitor) as synergistically cytotoxic in the six lines that displayed complete or partial resistance to PLX4720 alone. To understand the mechanism(s) of drug resistance and synergy, and to identify potential biomarkers for utilization of this combination, we have performed an extensive molecular analysis of the cell lines and their responses to treatment with the drugs, singly and in combination. Alamar blue was used to calculate the cytotoxic effects of the drugs, and the cells were profiled by gene expression microarrays and reverse phase protein microarrays, before and after drug treatment. DNA methylation microarrays, and exome sequencing were used to identify the epigenetic and genetic context of the lines. Unsupervised hierarchical clustering analysis of the cytotoxic effects of the single drug and combinatorial treatments revealed four well defined groups: synergistic to combination treatment, PLX4720 sensitive, lapatinib resistant, and insensitive to either drug. The cell lines were analyzed using principal component analysis on the basal gene expression profiles. This analysis revealed four major clusters whose membership was identical to the clusters observed in the cytotoxicity analysis, indicating a strong genotype-drug phenotype relationship (i.e. cell lines that showed similar basal transcriptional programs also demonstrated similar responses to treatment. Differential gene expression analysis was performed between the synergistic group and each of the other groups. The most prominent gene expression differences were between the synergistic and PLX4720 sensitive lines, which correlated with differences in MITF expression and transcriptional output. Changes in gene expression and phosphoproteome levels in response to PLX4720 and lapatinib treatment between the four groups were used to identify genes and pathways associated with drug resistance and synergy. Genes involved in apoptosis and cell cycle regulation were found that could play a role in conferring resistance and synergistic response to PLX4720 and lapatinib treatment. These differing patterns of adaptive responses provide insights into molecular mechanisms of drug resistance and synergy and a pathway to rational construction of drug combinations. Citation Format: Brian J. Capaldo, Devin G. Roller, Mark Axelrod, Alex Koeppel, Michael J. Weber, Aaron Mackey, Dan Gioeli, Stefan Bekiranov. Integrated molecular profiling of melanoma cell lines reveals genotype-drug phenotype associations giving insight on development of combination therapies. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4166. doi:10.1158/1538-7445.AM2014-4166
Published: 2014
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31. Abstract B29: Combinatorial screens with targeted inhibitors reveal diverse compensatory responses and mechanisms of adaptive resistance to therapy

Author: Devin G. Roller, Michael J. Weber, Aaron J. Mackey, Mark J. Axelrod, Mark R. Conaway, Brian J. Capaldo, and Daniel Gioeli
Subjects: Cancer Research, Cell signaling, Kinase, medicine.medical_treatment, Cancer, Pharmacology, Biology, medicine.disease, Targeted therapy, Oncology, Ribosomal protein s6, medicine, Cancer research, Signal transduction, Cytotoxicity, Combination drug
Abstract: The effectiveness of targeted inhibition of cell signaling can be blunted by compensatory signaling, which generates adaptive resistance mechanisms and reduces therapeutic responses. We have performed high-throughput combinatorial drug screening as a discovery tool to identify compensatory pathways that confer resistance to the cytotoxic effects of targeted therapy. We screened subsets of over 1,000 drug combinations in 14 different epithelial cell lines representing three distinct cancer lineages, and 19 melanoma cell lines, and assessed the ability of each combination to cause synergistic cytotoxicity. We focused on synergistic combinations because they point to mechanistic linkages between the signaling pathways, and also because of the possibility of improved therapeutic index in vivo. Drug substitution studies were used to validate the functionally important drug targets. Of the 84 combinations that caused robust synergy in multiple epithelial cell lines, none were synergistic in more than half of the lines tested, and we observed no pattern with respect to lineage specificity or mutational status of commonly altered oncogenes in the observed synergies. Within the melanoma panel, BRAF mutational status predicted response to single-agent BRAF inhibition, but did not predict synergistic drug combinations, which were different for each cell line. These results reflect the heterogeneity of genetic alterations and the plasticity of cell signaling networks even among cell lines of the same tissue of origin that contain the same predominant driver mutations. We suggest that there is not a sharp dichotomy between “driver” and “passenger” mutations, and that the biological responses to combination therapies are determined by functionally important modifier mutations that we term “back-seat drivers.” This hypothesis is supported by analysis of the transcriptomes and phosphoproteomes of cells treated with drugs singly and in combination, and by exome sequencing. Such analyses also can reveal critical nodes with the potential to function as effective single targets. We found that co-inhibition of EGFR and PI3 Kinase causes synergistic cytotoxicity in some epithelial cancer cell lines, and that phosphoproteomic analysis of signaling pathway responses revealed concordant synergistic inhibition of p70S6 Kinase in KU-7 bladder cancer cells. Using an epistasis paradigm, restoration of p70S6 Kinase signaling by expression of mutationally activated p70S6 Kinase resulted in protection from cytotoxicity, indicating that p70S6 Kinase is a critical node for enhanced cytotoxicity due to combination treatment. AT7867, a potent inhibitor of p70S6 Kinase, was able to inhibit phosphorylation of ribosomal protein S6 and induce cytotoxicity as effectively as the combination drug treatment. We suggest that p70S6 Kinase acts as a functionally important node within the EGFR/PI3 Kinase signaling network and is an attractive target for therapeutic intervention. Citation Format: Mark J. Axelrod, Devin Roller, Brian Capaldo, Aaron J. Mackey, Mark Conaway, Daniel Gioeli, Michael J. Weber. Combinatorial screens with targeted inhibitors reveal diverse compensatory responses and mechanisms of adaptive resistance to therapy. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Synthetic Lethal Approaches to Cancer Vulnerabilities; May 17-20, 2013; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(5 Suppl):Abstract nr B29.
Published: 2013
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32. Abstract 3400: Small molecule biological screens and global analytical approaches reveal the complexity of cancer cell signaling networks and the challenge of personalized medicine

Author: Michael J. Weber, Daniel Gioeli, Devin G. Roller, Aaron J. Mackey, Brian J. Capaldo, and Mark R. Conaway
Subjects: Neuroblastoma RAS viral oncogene homolog, Cancer Research, Cell signaling, Melanoma, Cell, Cancer, Biology, Lapatinib, medicine.disease, Cell biology, medicine.anatomical_structure, Oncology, Cancer cell, medicine, Cancer research, Vemurafenib, neoplasms, medicine.drug
Abstract: BRAF is mutationally activated in 50% of melanomas with a variety of data indicating its role as a driver of malignancy. However, while patients with BRAF mutations respond to vemurafenib, most responses are not long lasting. Moreover, the 25% of melanomas harboring an NRAS activating mutation are resistant de novo to BRAF inhibitors. In order to identify effective drug combinations that increase the effectiveness of BRAF inhibition, we used combinatorial small molecule biological screens and global analytical approaches to identify compensatory and redundant pathways. We screened a drug library for combinations with BRAF or MEK inhibitors that cause synergistic cytotoxicity in melanoma cell lines. Although BRAF mutational status predicted sensitivity to inhibitors of BRAF, each BRAF mutant line differed in the combination of drugs that induced synergistic cytotoxicity. One robust and clinically relevant pair identified was the BRAF inhibitor PLX4720 and the EGFR/HER2 inhibitor lapatinib. This drug combination was synergistic in half of the BRAF mutant cell lines tested, including a line that was completely resistant to PLX4720. The combination was also effective in the two NRAS mutant lines tested. Xenograft experiments testing the combination of PLX4720 and lapatinib revealed a significant reduction in tumor volume compared to either single agent alone in both PLX4720 sensitive and resistant BRAF mutant tumors, providing preclinical data supporting the efficacy of this drug combination. Synergy of the PLX4720-lapatinib combination did not correlate with EGFR/HER2 expression, activity state, or mutational status. This further indicates that sensitivity of melanoma to drug combinations is not determined by the targeted oncogenic drivers, but by the existence of unique secondary, compensatory survival responses. We hypothesize that mutationally activated BRAF is wired into the constitutive signaling network differently in each of these cells, presumably a consequence of the diverse secondary mutations that characterize melanomas. Consistent with this, whole exome-sequencing data revealed a diversity of somatic variation among the synergistic and non-synergistic melanomas, while gene expression analysis demonstrated both distinct, line-specific basal transcriptional profiles and the lack of common gene expression signatures in melanomas synergistically inhibited by treatment with PLX4720-lapatinib. Collectively, these data point out the extraordinary robustness of cancer cell signaling networks and the challenges of individualizing therapies. This study has uncovered novel functional drug combinations and suggests that the underlying signaling networks that control responses to targeted agents can vary substantially depending on unexplored components of the cell genotype and cell signaling network. Citation Format: Devin Roller, Brian Capaldo, Aaron J. Mackey, Mark Conaway, Michael J. Weber, Daniel G. Gioeli. Small molecule biological screens and global analytical approaches reveal the complexity of cancer cell signaling networks and the challenge of personalized medicine. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3400. doi:10.1158/1538-7445.AM2013-3400
Published: 2013
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33. Genetic susceptibility to Leishmania: IL-12 responsiveness in TH1 cell development

Author: Mehmet L. Guler, Robert G. Steen, James D. Gorham, Chyi-Song Hsieh, Aaron J. Mackey, William F. Dietrich, and Kenneth M. Murphy
Subjects: medicine.medical_treatment, Leishmaniasis, Cutaneous, Mice, Transgenic, Biology, Lymphocyte Activation, Interleukin 21, Interferon-gamma, Mice, Th2 Cells, Antigen, medicine, Cytotoxic T cell, Animals, Interferon gamma, Genetic Predisposition to Disease, Interleukin 4, Cells, Cultured, Leishmania major, Mice, Inbred BALB C, Multidisciplinary, Receptors, Interleukin-2, T lymphocyte, Th1 Cells, Interleukin-12, Coculture Techniques, Immunity, Innate, Cytokine, Phenotype, Immunology, Interleukin 12, Interleukin-4, medicine.drug, Signal Transduction
Abstract: The genetic background of T lymphocytes influences development of the T helper (TH) phenotype, resulting in either resistance or susceptibility of certain mouse strains to pathogens such as Leishmania major. With an in vitro model system, a difference in maintenance of responsiveness of T cells to interleukin-12 (IL-12) was detected between BALB/c and B10.D2 mice. Although naive T cells from both strains initially responded to IL-12, BALB/c T cells lost IL-12 responsiveness after stimulation with antigen in vitro, even when cocultured with B10.D2 T cells. Thus, susceptibility of BALB/c mice to infection with L. major may derive from the loss of the ability to generate IL-12-induced TH1 responses rather than from an IL-4-induced TH2 response.
Published: 1996

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33 results on '"Aaron J. Mackey"'

1. Optimization of techniques for multiple platform testing in small, precious samples such as human chorionic villus sampling

2. Targeted Deep Sequencing in Multiple-Affected Sibships of European Ancestry Identifies Rare Deleterious Variants in PTPN22 That Confer Risk for Type 1 Diabetes

3. Combinatorial drug screening and molecular profiling reveal diverse mechanisms of intrinsic and adaptive resistance to BRAF inhibition in V600E BRAF mutant melanomas

4. An Islet-Targeted Genome-Wide Association Scan Identifies Novel Genes Implicated in Cytokine-Mediated Islet Stress in Type 2 Diabetes

5. Pharmacotoxicology of Clinically-Relevant Concentrations of Obeticholic Acid in an Organotypic Human Hepatocyte System

6. Synthetic Lethal Screening with Small-Molecule Inhibitors Provides a Pathway to Rational Combination Therapies for Melanoma

7. Leptin Protects Host Cells from Entamoeba histolytica Cytotoxicity by a STAT3-Dependent Mechanism

8. Insights into evolution of multicellular fungi from the assembled chromosomes of the mushroom Coprinopsis cinerea ( Coprinus cinereus )

9. Periplasmic Proteins of the Extremophile Acidithiobacillus ferrooxidans

10. Transcriptional Profiling Suggests That Nevirapine and Ritonavir Cause Drug Induced Liver Injury Through Distinct Mechanisms in Primary Human Hepatocytes

11. An In Vitro Cynomolgus Vascular Surrogate System for Preclinical Drug Assessment and Human Translation

12. OrthoMCL-DB: querying a comprehensive multi-species collection of ortholog groups

13. The genome of Cryptosporidium hominis

14. Entamoeba histolytica: sequence conservation of the Gal/GalNAc lectin from clinical isolates

15. BAYSIC: a Bayesian method for combining sets of genome variants with improved specificity and sensitivity

16. Whole-exome sequencing identifies rare and low-frequency coding variants associated with LDL cholesterol

17. Genetic mapping of a murine locus controlling development of T helper 1/T helper 2 type responses

18. CRP: Cleavage of Radiolabeled Phosphoproteins

19. Global changes in gene expression during compatible and incompatible interactions of cowpea (Vigna unguiculata L.) with the root parasitic angiosperm Striga gesnerioides

20. The Drosophila melanogaster Genetic Reference Panel

21. Evigan: a hidden variable model for integrating gene evidence for eukaryotic gene prediction

22. ToxoDB: an integrated Toxoplasma gondii database resource

23. Assessing performance of orthology detection strategies applied to eukaryotic genomes

24. Systems Analysis of Adaptive Responses to MAP Kinase Pathway Blockade in BRAF Mutant Melanoma

25. The Genome of the Sea Urchin Strongylocentrotus purpuratus

26. Insights into social insects from the genome of the honeybee Apis mellifera

27. The transcriptome of Toxoplasma gondii

28. Composite genome map and recombination parameters derived from three archetypal lineages of Toxoplasma gondii

29. A strategy for the rapid identification of phosphorylation sites in the phosphoproteome

30. Abstract 4166: Integrated molecular profiling of melanoma cell lines reveals genotype-drug phenotype associations giving insight on development of combination therapies

31. Abstract B29: Combinatorial screens with targeted inhibitors reveal diverse compensatory responses and mechanisms of adaptive resistance to therapy

32. Abstract 3400: Small molecule biological screens and global analytical approaches reveal the complexity of cancer cell signaling networks and the challenge of personalized medicine

33. Genetic susceptibility to Leishmania: IL-12 responsiveness in TH1 cell development

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